Design and Implementation of an Optimal Travel Route Recommender System on Big Data for Tourists in Jeju

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1 processes Article Design Implementtion n Optiml Trvel Route Recommender System on Big Dt for Tourts in Jeju Lei Hng, Sng-Hun Kng, Wenqun Jin ID Do-Hyeun Kim * Deprtment Computer Engineering, Jeju Ntionl University, Jeju 63243, Kore; hnglei@jejunu.c.kr (L.H.); myks790@gmil.com (S.-H.K.); wenqun.jin@jejunu.c.kr (W.J.) * Correspondence: kimdh@jejunu.c.kr; Tel.: Received: 24 July 2018; Accepted: 8 August 2018; Publhed: 17 August 2018 Abstrct: A recommender system currently pplied in mny different domins, seeking provide users with recommendtion services ccording ir personlized preferences relieve ring online informtion congestion. As number mobile phone users lrge growing, mobile urt guides hve ttrcted considerble reserch interest in recent yers. In th pper, we propose n optiml trvel route recommender system by nlyzing dt hry previous users. The open dtset used covers trvel dt from thouss mobile urts who vited Jeju in full yer. Our pproch not only personlized users preferences but lso ble recommend trvel route rr thn individul POIs (Points Interest). An ssocition rule mining-bsed pproch, which tkes in ccount contextul informtion (dte, seson plces lredy vited by previous users), used produce trvel routes from lrge dtset. Furrmore, ensure resonbility recommendtion, genetic lgorithm optimiztion pproch proposed find optiml route mong m. Finlly, mobile urt cse study implemented in order verify fesibility pplicbility proposed system. Th ppliction embeds grphic mp for plotting trvel route provides detiled informtion ech trvel spot s well. The results th work indicte tht proposed system hs gret potentil for trvel plnning preprtion for mobile users. Keywords: trvel route recommendtion; ssocition rule mining; route optimiztion; mobile; trvel plnning 1. Introduction Gret opportunities hve been fered with flourhment socil medi, such s Fcebook Twitter, for ddressing mny chllenging problems, such s trvel recommendtions. Trvel recommendtion n importnt ppliction field in both reserch industry. Mny trvel sites provide informtion on proposl, which written by or users help user crete unique trvel pln. For exmple, TripSpot [1] fmous trvel site feturing more thn 100 country guides through which users cn get vriety informtion, including urm informtion, destintion ides, mps, wer forecsts, etc. Nowdys, more more people join socil medi network shre ir phos which records users dily life trvel experience. These phos contin heterogeneous metdt (e.g., tgs, dte tken, ltitude etc.) which re not only useful for relible POIs (Points Interest) [2] but lso provide gret opportunity recommend personlized trvel routes ccording users preferences. Two min chllenges hve be noted when considering trvel recommendtions. First, trvel recommender systems should provide personlized trvel recommendtions bsed on user interest Processes 2018, 6, 133; doi: /pr

2 Processes 2018, 6, since preference POIs vries from user user [3]. For exmple, some people my prefer hricl sites such s temples pvilions, while ors re interested in city lmrks such s high-re buildings. Mny or ttributes including seson viting times re lso helpful provide personlized trvel recommendtions, more so thn picl trvel interest. Secondly, recommendtion sequentil trvel route fr more complex time-consuming thn individul POIs, since dtnce between loctions or opening times different POIs should be considered [4]. Exting pproches on trvel recommendtion cn be clssified in two kinds in terms services y render, which re destintion-bsed recommendtions route-bsed recommendtions. The former pproch centered on recommendtion single destintion which best meets user s interest. One typicl cses PersonlTour [5], which used by trvel gencies help ir cusmers find best trvel pckges ccording ir preferences. Once recommendtion process completed, lt rted options presented user. The ltter pproch not only provides lt plces tht fit better with user s preferences, but lso help urts crete route contining severl destintions. Trip-plnning [6] time-consuming tsk re lwys dem for system tht ble provide urt loctions ccording users interests preferences. Pho2Trip [7] leverges geo-tgged phos suggest cusmized trvel routes ccording user s preference, it lso enbles user input personl preference in n interctive mnner. However, se exting recommendtion pproches only work bsed on user pic interest mining but without considering or personlized ttributes such s preferred sesons. Some m fcilitte route mining using personlized trvel ttributes, but without vlidting resonbility route. It cnnot be resonble recommendtion, for exmple, if recommended POIs route in one dy re locted in different directions. As result, high relible trvel route recommendtion still formidble chllenge for exting trvel recommendtion pproches. The contribution th work cn be divided in three-fold structure: first, we propose novel route recommendtion system tht ims provide trvel routes depending on previous users hricl experience. An ssocition rule mining-bsed pproch utilized, which used dcover frequent routes between different urts in lrge dtset. Secondly, genetic lgorithm-bsed route optimiztion pproch designed find optiml trvel route by using dtnce fitness function. Lstly, mobile urt cse study implemented s pro concept vlidte performnce proposed system. Numerous ctivities snpshots cse study re presented dcussed. The reminder th pper orgnized s follows: Section 2 dcusses some min pproches used in trvel recommender systems overviews some similr relted reserch works. Section 3 gives n overview proposed system s conceptul rchitecture detils ech component system. Section 4 overviews experimentl setup reports results proposed trvel route recommender system. Section 5 provides some insight in implementtion cse study. Section 6 overviews implementtion mobile urt cse study with vrious snpshots. Section 7 outlines significnce proposed work by comprtive nlys proposed work with some exting works. Finlly, Section 8 concludes pper dcusses future direction reserch. 2. Relted Work Th section overviews some recent studies in trvel recommendtion points out differences between designed work exting works. Generl trvel recommender systems recommend trvel routes in terms populrity POIs or routes [8]. Recently, personlized trvel recommendtions hve ttrcted more ttention. Collbortive Filtering (CF) [9], Mrkov Chins [10] mtrix fcriztion [11] re three min pproches, widely used in personlized trvel recommendtion. The most fmous method loction-bsed collbortive filtering (LCF) which

3 Processes 2018, 6, mesures similr socil users bsed on loction co-occurrence previously vited POIs n rnks POIs bsed on similr users viting records. Clements et l. [12] model co-occurrence with Gussin density estimtion trvel routes re recommended ccording similr users voting. Cheng et l. [13] built mtrix user loction by using user check-in informtion on loctions multi-center Gussin model ws dopted model probbility nlyze user s check-in behvior, including check-in frequency over sptil, which re used predict unknown frequency se unvited loctions. Anor project clled GoThere [14] context preference wre trvel guide tht suggests significnt urt destintions bsed on users preferences current surrounding context generted from well-known socil medi reposiry Flicker [15]. The user preferences on content trvel spots re dtinguhed from user preferences on trvel spots mselves, which motivtes n independent loction model users content preferences loction-wre model users loction preferences trvel spots. However, loction-bsed pproches my fce dt sprse sue when users hve very few loction records so tht mining similr users could be very difficult. A pic model effective for solving dt sprse problem, for exmple, n uthor pic model-bsed collbortive filtering method [16] proposed mine ctegory user pic interest (i.e., culturl, cityscpe, lmrk, more) simultneously so s fcilitte comprehensive recommendtions for socil users. A loction-bsed preference-wre trvel recommendtion system presented by uthors [17] by using weighted ctegory hierrchy model ech individul s personl preferences, from lerning n itertive lerning model in ir fline module. However, personlized trvel route recommendtion [18] more convenient for users s conventionl pproches only provide n individul recommendtion. For exmple, uthors [19] propose new trip-plnning method recommend trvel route tht relted users in given context. Trvel preferences from users trvel hry in one loction re used recommend urt loctions in or cities. Anor project [20] focuses on sentimentl ttributes loction proposes mining-bsed method. They use sentiment-bsed mining lgorithm mine user interested loctions with obvious sentimentl ttributes so s recommend trvel routes or users. Go et l. present trvel guidnce system [21], which cn umticlly recognize rnk lmrks for urts. They pplied novel lmrk rnking pproch by using geotg info in Flicker user knowledge from Yhoo Trvel Guide. The proposed method selected populr lmrks computed probbility tg being lmrk nme. In recent yers, studies trvel route recommendtion tht contin more context ttributes hve shown more effective performnce thn picl interest. For exmple, Yun et l. exploited both geogrphicl temporl influence in time-wre recommendtion. They proposed geogrphicl-temporl influence-wre grph which imed t modeling check-in records, geogrphicl influence, temporl influence recommend POIs user t given time [22]. Chen et l. focus on leverging urts ttributes from pho contents [23]. They mined demogrphics for different loctions trvel pths n Byesin lerning frmework ws introduced for furr giving mobile recommendtion on spot. To best our knowledge, se exting studies relted trvel recommendtion eir recommend destintion considering only user preferences or put effort on route mining considering personl trvel ttributes. Furrmore, se trvel ttributes hve not been mined umticlly few m optimize recommendtion ensure relibility vilbility. It necessry fully dopt ll se fetures in designing trvel route recommendtion system thus improve performnce. 3. Proposed Optiml Trvel Route Recommender System Architecture 1 illustrtes conceptul rchitecture proposed optiml trvel route recommender system. In th work, recommender system imed t helping users mke personlized trvel decions bsed on dt hry from previous users. To chieve th purpose, proposed system

4 Processes 2018, 6, umticlly nlyzes dt resources mine trvel routes ccording personl trvel interests (i.e., preferred seson, wer, etc.). Then system serches best trvel route provides recommendtion Processes 2018, 6, x FOR PEER mobile REVIEW users Proposed Optiml Trvel Route Recommender System Dt Resources Input Vribles CSV Assocition Rule Mining bsed Route Recommendtion Third Prty APIs Moving Pth Seson Spot Informtion Route Genertion Genetic Algorithm bsed Route Optimiztion Route Recommendtion CSV Geo Loction Best Route Serch Fitness Function Mobile User 1. Conceptul rchitecture optiml trvel route recommender system. 1. Conceptul rchitecture optiml trvel route recommender system. More specilly, dt resources re in Comm Seprted Vlues (CSV) formt, contining More specilly, dt resources re in Comm-Seprted Vlues (CSV) formt, contining multiple vlues such s moving pth, seson, geoloction informtion ech trvel spot. multiple vlues such s moving pth, seson, geoloction informtion ech trvel These vlues re tken s inputs route recommender system on which ssocition rule spot. These vlues re tken s inputs route recommender system on which ssocition mining bsed route recommendtion pproch pplied find trvel routes. The genetic rule mining-bsed route recommendtion pproch pplied find trvel routes. The genetic lgorithm bsed route optimiztion solution tkes se routes find optiml routes by referring lgorithm-bsed route optimiztion solution tkes se routes find optiml routes by referring fitness function. These optiml routes re lso mintined s CSV document, which fitness function. These optiml routes re lso mintined s CSV document, which ingested by mobile ppliction thus mkes m vible in intuitive grphicl mp. ingested by mobile ppliction thus mkes m vible in intuitive grphicl mp. Furrmore, vrious third prty Appliction Progrm Interfces (APIs) re utilized provide Furrmore, vrious third-prty Appliction Progrm Interfces (APIs) re utilized provide different functionlities such s wer forecst video serching. The detiled processes different functionlities such s wer forecst video serching. The detiled processes ssocition rule mining bsed route recommendtion genetic lgorithm bsed route optimiztion ssocition re rule dcussed mining-bsed in following route recommendtion subsections. genetic lgorithm-bsed route optimiztion re dcussed in following subsections Assocition Rule Mining Bsed Trvel Route Recommendtion 3.1. Assocition Rule Mining Bsed Trvel Route Recommendtion Assocition rule mining concentrtes on finding rules which will predict occurrence n Assocition rule mining concentrtes on finding rules which will predict occurrence n item item bsed on occurrences or items in trnsction [24]. The fct tht two items re found bsed on occurrences or items in trnsction [24]. The fct tht two items re found be be relted mens co occurrence but not cuslity. Assocition rule mining [25] cn be used in relted mens co-occurrence but not cuslity. Assocition rule mining [25] cn be used in bsket dt bsket dt nlys, eductionl dt mining, clssifiction, clustering etc. Agrwl et l. introduce nlys, eductionl dt mining, clssifiction, clustering etc. Agrwl et l. introduce ssocition ssocition rules [26] dcover regulrities between products in lrge scle trnsction dt from rules [26] dcover regulrities between products in lrge-scle trnsction dt from supermrkets. supermrkets. For exmple, rule {egg} {burger, pot} would indicte tht cusmer buys For exmple, rule {egg} {burger, pot} would indicte tht cusmer buys eggs, y re eggs, y re very likely lso buy burger met potes ger. In th pper, we pply very likely lso buy burger met potes ger. In th pper, we pply principles principles ssocition rule mining dcover reltions between trvel spots in lrge trvel ssocition rule mining dcover reltions between trvel spots in lrge trvel hry dtset, hry dtset, strong rules re identified using some mesures interest vlue. 2 gives strong rules re identified using some mesures interest vlue. 2 gives s overview s overview rchitecture proposed ssocition rule mining bsed pproch for trvel rchitecture proposed ssocition rule mining-bsed pproch for trvel route recommendtion. route recommendtion. The designed ppliction consts three modules: route extrction module, The designed ppliction consts three modules: route extrction module, frequent route mining frequent route mining module, strong route genertion module. module, strong route genertion module. Apriori clssic lgorithm for lerning ssocition rules in computer science dt mining. Apriori clssic lgorithm for lerning ssocition rules in computer science dt mining. Th pproch [27] ws first designed operte on dtbses contining trnsctions which re Th pproch [27] ws first designed operte on dtbses contining trnsctions which re composed set items or itemset (for exmple, collections items purchsed by cusmers). composed set items or itemset (for exmple, collections items purchsed by cusmers). Some or lgorithms re designed for finding ssocition rules in dt hving no trnsctions Some or lgorithms re designed for finding ssocition rules in dt hving no trnsctions (Winepi (Winepi Minepi) [28], or hving no timestmps (DNA sequencing). Given threshold C, Minepi) [28], or hving no timestmps (DNA sequencing). Given threshold C, Apriori Apriori lgorithm identifies item sets which re subsets t lest C trnsctions in dtbse. lgorithm identifies item sets which re subsets t lest C trnsctions in dtbse. Constrints Constrints on vrious mesures significnce interest cn be used in order select credible rules from set ll possible rules. The best known constrints re minimum thresholds on support confidence.

5 Processes 2018, 6, on vrious mesures significnce interest cn be used in order select credible rules from set ll possible rules. The best-known constrints re minimum thresholds on support confidence. Processes 2018, 6, x FOR PEER REVIEW 5 20 Assocition Rule bsed Trvel Route Recommendtion Trvel Hry Dtset Route Extrction Pth Yer Month Loction User ID... Frequent Route Mining Strong Route Genertion Trvel Routes Route 1 Route 2 Route Architecture ssocition rule mining trvel route recommendtion module. 2. Architecture ssocition rule mining trvel route recommendtion module. The n itemset X defined s proportion trnsctions in dt set which contins The supp(x) itemset. n itemset X defined s proportion trnsctions in dt set which contins itemset. number o f (1) trnsctions which contin itemset X supp(x) = (1) number o f tl trnsctions The confidence rule ( ) with respect set trnsctions T, defined s proportion The confidence trnsctions rule ( xtht y contins ) with respect X which lso set contins trnsctions Y. T, defined s proportion trnsctions tht contins X which lso contins Y. / (2) The pseudo code for lgorithm con f (X Y) given = supp(x below for Y)/supp(X) set itemsets, lgorithm ttempts (2) find subsets which re common t lest minimum number C itemsets. A botm up pproch The pseudo utilized code in for Apriori, lgorithm where frequent givensubsets below re for extended set itemsets, one item t lgorithm time (lso ttempts known s find cidte subsets genertion), which re common groups t lest cidtes minimum re tested number ginst C by itemsets. dt. Th A botm-up process pproch terminted by utilized lgorithm in Apriori, until where no furr frequent successful subsets re extensions extendedre one found. item A t bredth first time (lso known serch s strtegy cidte used genertion), count support groups itemsets cidtes efficiently re tested ginst counts by cidte dt. Th itemsets processre terminted sred tree by structure. lgorithm Cidte until noitemsets furr successful length k extensions re generted re found. by joining A bredth-first itemsets serch length strtegy k 1 those used who count hve n support infrequent itemsets subpttern efficiently re pruned. The counts cidte cidte itemsets itemsets contin re ll sred frequent in k length tree structure. itemsets Cidte ccording itemsets downwrd length kclosure re generted lemm. by After joining tht, itemsets frequent itemsets length kre 1determined those who mong hve ncidtes infrequent by subpttern scnning re pruned. trnsction Thedtbse. cidteassocition itemsets contin rules re ll frequent simultneously k-lengthrequired itemsets ccording stfy user specified downwrd minimum closurethreshold lemm. After support tht, frequent confidence. itemsets re The determined method used mong generte cidtes ssocition by scnning rules be split trnsction up in dtbse. two step Assocition process s rules follows: re simultneously pply minimum required support stfy threshold user-specified find ll frequent minimum itemsets threshold n support form rules confidence. from se The frequent method itemsets used in generte terms ssocition minimum rules confidence cn be split constrint. up inwe two-step hve defined process sfollowing follows: pply terminologies minimum for support pseudo threshold code such s find : ll frequent frequent items, itemsets T: dtbse, nms: formminimum rules from support, se frequent : cidte itemsets generted in terms from minimum, : k size confidence cidte set, constrint. t: trnsction, We hve : defined cidte following with terminologies minimum support, for pseudo : counting code such frequent s L 1 : frequent itemsets. items, T: dtbse, MS: minimum support, C g : cidte generted from L k 1, C k : k size cidte set, t: trnsction, C m : cidte C k with minimum support, U k : counting frequent itemsets.

6 Processes 2018, 6, x FOR PEER REVIEW 6 20 Processes 2018, 6, Pseudo Code for Apriori (T, MS) Input Pseudo(T, Code MS) for Apriori (T, MS) Output: Input (T, MS) Begin: Output: U k Begin: {T} L 1 {T} for k 2 do { for k 2 L k 1 do { C k //crtesin product Lk 1 Lk 1 eliminting ny k 1 infrequent size items set C g //crtesin product L k 1 L k 1 eliminting ny k 1 infrequent size items set for ech t in T do { for ech t in T do { L k C m od } od } od } od } Return U k Return Generlly, trvel route sequence ttrctions in specific order, for exmple, in terms Generlly, trvel route sequence ttrctions in specific order, for exmple, in terms order from left right, left-most point within sequence considered s strt point order from left right, left most point within sequence considered s strt point right-most one s endpoint. Route extrction first step in proposed trvel route right most one s endpoint. Route extrction first step in proposed trvel route recommendtion pproch, which processes retrieving dt out dt sources recombines se recommendtion pproch, which processes retrieving dt out dt sources recombines se dt generte routes for furr dt processing or dt srge, s shown in 3. In our dt generte routes for furr dt processing or dt srge, s shown in 3. In our proposed system, we extrct more fetures such s geo-loction, pth, user Id, yer, month proposed system, we extrct more fetures such s geo loction, pth, user Id, yer, month informtion from trvel dtset or thn individul ttrctions generte route set on informtion from trvel dtset or thn individul ttrctions generte route set on which which we re pplying Apriori lgorithm. The detils used dtset will be illustrted in we re pplying Apriori lgorithm. The detils used dtset will be illustrted in next section. next section. Geo Loction, Pth, User ID, Yer, Month Trvel Hry Dtset Route Extrction Route1 Route2 Route3 Route4... Route n 3. Route extrction from trvel dtset. The tsk for frequent route mining find ll common sets individul ttrctions by which compose trvel routes tht hve t lest minimum support. To generte frequent routes, following re requirements for n effective cidte genertion procedure: First, it should void generting o mny unnecessry cidtes tht key sue ffecting lgorithm performnce. Secondly, it it must must ensure ensure tht tht cidte cidte route route complete complete lstly lstly it should it should not generte not generte sme sme route more route thn more once. thn The once. Brute-Force The Brute Force [29] method [29] method used in used proposed in pproch proposed generte pproch ll generte possibilities ll possibilities routes. Ech route routes. in Ech lttice route in cidte lttice frequent cidte route frequent support route for echsupport cidte for ech counted cidte by scnning counted by dtset scnning order dtset mtchin ech order route mtch ginst ech route ginst ( ) every cidte. every cidte. For given d items, number cidte routes generted t level k equl For given d items, number cidte routes generted t level k equl dk, overll, th method overll defined th in method Eqution defined (3): in Eqution (3): d ( ) O( ) d k = O( d 2 2 d 1) (3) (3) k k=1

7 Processes 2018, 6, The set possible route sets power set over I hs size 2 n 1 which size grows Processes 2018, 6, x FOR PEER REVIEW 7 20 exponentilly in number items n in I. An efficient serch desired minimize number cidtes The or reduce set possible size route dtsets. The power most common set over pproch I hs size reduce 2 1 number which cidtes size usinggrows Apriori exponentilly principle in [30]. Th number principle items cnn be in used I. An efficient simplify serch pttern desired genertion minimize process when mining number ptterns cidtes in dt sets. or reduce Th principle size gurntees dtsets. The tht most if cidte common pproch set frequent, reduce n ll its number cidtes using Apriori principle [30]. Th principle cn be used simplify subsets must lso be frequent thus no infrequent itemset cn be subset. The Apriori principle holds pttern genertion process when mining ptterns in dt sets. Th principle gurntees tht if due following property support mesure s shown in Eqution (4): cidte set frequent, n ll its subsets must lso be frequent thus no infrequent itemset cn be subset. The Apriori principle holds due following property support mesure s X, Y : (X Y) s(x) s(y) (4) shown in Eqution (4): Th verified using support mesure, : becuse support n itemset never exceeds (4) tht its subsets, Th which verified lso using known ssupport nti-monone mesure becuse property support support n [31]. itemset The never Apriori exceeds tht prcticl implementtion its subsets, which principle. lso known The lgorithm s nti monone in th illustrtion property plotted support mine [31]. ll The frequent Apriori routes in dtbse. prcticl Multiple implementtion itertions re performed principle. The serches lgorithm in in dtbse th illustrtion for finding plotted frequent mine routes ll where k-item frequent routes re routes usedin generte dtbse. k + Multiple 1-routes. itertions 4re illustrtes performed genertion serches in frequent dtbse route for set usingfinding Apriori frequent lgorithm. routes The where thresholds k item routes re minimum used generte support k + 1 routes. minimum confidence 4 illustrtes should be predetermined genertion before frequent lgorithm route set strts using Apriori find frequent lgorithm. routes. The thresholds After tht, it scns minimum dtset support compute minimum confidence should be predetermined before lgorithm strts find frequent routes. After support ech ttrction. These ttrctions, with support which equl or greter thn tht, it scns dtset compute support ech ttrction. These ttrctions, with support predefined minimum support, re dded 1-route frequent sets R1 th process terminted t which equl or greter thn predefined minimum support, re dded 1 route frequent sets point R1 where th process no more frequent terminted route t sets point rewhere generted. no more frequent route sets re generted. Strt N Scn dtset get support S ech ttrction S>=min_support Y Add frequent 1 route, R1 Use R(k 1) join R(k 1) generte set k item route N Scn dtset get support S ech cidte k item route S >= min_support Y Add k frequent route sets Generted set = null Y End 4. Flowchrt frequent route set genertion. 4. Flowchrt frequent route set genertion.

8 Processes 2018, 6, Given frequent route set R, requirement when route cn be dded strong route find Processes ll nonempty 2018, 6, x FOR subsets PEER REVIEW which meet minimum confidence condition. As in 8 20 frequent route genertion, we lso need look for wy generte strong routes efficiently. In th pper, Given frequent route set R, requirement when route cn be dded strong route we generte strong routes by merging two routes tht shre sme prefix in rule consequent. find ll nonempty subsets which meet minimum confidence condition. As in frequent Followingroute rules genertion, shown in we lso need 5 re look used for wy generte generte strong strong routes from efficiently. frequent In th pper, route set: we generte strong routes by merging two routes tht shre sme prefix in rule consequent. For ech Following frequent rules shown route in set R, find 5 re ll used nonempty generte subset strong routes R. from frequent route set: For ech For non-empty ech frequent subset route Sset R, R, find write ll nonempty strong subset route R. S (R-S) if support count R/support count For S ech minimum non empty confidence. subset S R, write strong route S (R S) if support count R/support count S minimum confidence. Strt Generte ll nonempty subsets for ech frequent route set R For ech nonempty subsets R, find confidence C S N C >= min_confidence Y Add strong routes End 5. Flowchrt strong route genertion. 5. Flowchrt strong route genertion Genetic Algorithm Bsed Trvel Route Optimiztion 3.2. Genetic Algorithm A genetic Bsed lgorithm Trvel [32] Route serch Optimiztion heurtic tht inspired by Chrles Drwin s ory nturl evolution. It frequently used find optiml or ner optiml solutions difficult problems A genetic in reserch lgorithm in mchine [32] lerning, serchwhich heurtic would tht orwe inspired result in byspending Chrles Drwin s lifetime solving ory nturl evolution. problem. It frequently Bsiclly, genetic used lgorithm find optiml opertes on or finite ner-optiml popultion solutions chromosomes difficult or bit strings. problems in reserch The serch in mchine mechnm lerning, [33] minly which composed would orwe three phses result s follows: in spending fitness evlution lifetime ech solving chromosome, selection prent chromosomes, ppliction muttion recombintion problem. Bsiclly, genetic lgorithm opertes on finite popultion chromosomes or bit strings. operrs prent chromosomes. The next genertion formed by new chromosomes derived The serchfrom mechnm se opertions, [33] minly process composed reduplicted three until phses system s chieves follows: purpose. fitnessth evlution pper ech chromosome, utilizes selection principles prent genetic chromosomes, lgorithm solve ppliction optimiztion problem muttion in trvel route recombintion operrsrecommendtion. prent chromosomes. The next genertion formed by new chromosomes 6 bsiclly defines flowchrt s n expression trvel route optimiztion using derived from se opertions, process reduplicted until system chieves purpose. genetic lgorithm. The process begins with set individuls which clled popultion. In Th pper utilizes proposed trvel principles route optimiztion genetic model, lgorithm ech individul solve solution optimiztion chrcterized problem by set in trvel route recommendtion. trvel spots known s Genes, which contins geo informtion such s longitude ltitude. For given 6 bsiclly N trvel spots, defines ech plce flowchrt identified s with n expression unique numeric lbel trvel in route rnge optimiztion 0 N 1, using where order route defined from left right. For exmple, one route contins 4 plces (0, genetic lgorithm. The process begins with set individuls which clled popultion. In 1, 2, 3, 4), where strt loction 0 end loction 4, cn be encoded s follows: proposed trvel route optimiztion model, ech individul solution chrcterized by set trvel spots known s Genes, which contins geo-informtion such s longitude ltitude. For given N trvel spots, ech plce identified with unique numeric lbel in rnge 0 N 1, where order route defined from left right. For exmple, one route contins 4 plces (0, 1, 2, 3, 4), where strt loction 0 end loction 4, cn be encoded s follows: plce 0 plce 1 plce 2 plce 3 plce 4

9 Processes 2018, 6, x FOR PEER REVIEW 9 20 Processes 2018, 6, Evlution core component in genetic lgorithm, which ccepts popultion generte Evlution core component in genetic lgorithm, which ccepts popultion new genertion. Selection, crossover muttion re n determined by evlution. The fitness generte new genertion. Selection, crossover muttion re n determined by evlution. The function defines function which tkes cidte solution problem s input produces fitness function defines function which tkes cidte solution problem s input s output how fit solution with respect problem in considertion. The fitness function produces s output how fit solution with respect problem in considertion. The fitness llows system determine which individuls will be selected for recombintion muttion t function llows system determine which individuls will be selected for recombintion ech phse genetic lgorithm, where bsic rule tht higher fitness vlue, better muttion t ech phse genetic lgorithm, where bsic rule tht higher fitness individul. In th pper we define dtnce fitness function for solution trvel route vlue, better individul. In th pper we define dtnce fitness function for solution optimiztion. For given route p 1, p 2,, p n, tl dtnce defined s shown in Eqution (5). trvel route optimiztion. For given route,,,, tl dtnce defined s shown in Eqution (5). N tl dtnce = dt(p i, p i+1 ) (5) i=1, (5) where dt(p i, p i+1 ) dtnce between plce i plce i + 1. In order describe shorter routes where with higher, fitness score, dtnce simple between fitness plce function i tht plce clcultes i + 1. In order inverse describe vlue shorter route dtnce routes with used higher s shown fitness in score, Eqution simple (6). fitness function tht clcultes inverse vlue route dtnce used s shown in Eqution (6). 1 f itness = (6) tl dtnce 1 (6) Input Vrible Output Vrible Ltitude Longitude Optiml Route Genetic Algorithm bsed Trvel Route Optimiztion Popultion Chromosome 1, Chromosome 2,, Chromosome N Algorithm Constrints: Popultion size Muttion probbility Tournment size Muttion Genetic Operrs Evlution Dtnce Fitness Function Crossover Selection 6. The process flow genetic lgorithm bsed trvel route optimiztion. 6. The process flow genetic lgorithm-bsed trvel route optimiztion. Prent selection process selecting prents which mte recombine crete fspring for Prent next selection genertion. Individul process solutions selecting re prents selected which through mte schstic recombine fitness process crete fspring with for principle next tht genertion. fitter solutions Individul hve solutions higher re possibility selected through be selected schstic s prents. fitness In th process pper, with we pply principle urnment tht fitter selection solutions method hve [34] higher s illustrted possibility in be selected 7, where s prents. number In individuls th pper, we re pply selected urnment from popultion selection method romly [34] s illustrted best one in selected 7, where s number winner for individuls crossover, re selected sme process from popultion repeted until romly next prent best one selected. selected Tournment s winner size for lso crossover, clled urnment sme process pressure repeted s until size grows next lrger, prent selected. less chnce Tournment wek individuls size lso clled be urnment selected. In pressure th pper, s we set size grows urnment lrger, size less 5%, chnce which equls wek individuls 5% popultion. be selected. In th pper, we set urnment size 5%, which equls 5% popultion.

10 Processes 2018, 6, Processes 2018, 6, x FOR PEER REVIEW Fitness Vlue 1 3 Route Chromosome Q A A 4 Z Select number route Pick best s prent 5Processes 2018, 6, x FOR W PEER REVIEW chromosomes t romly route chromosome Fitness Vlue S Route Chromosome S F 6 1 X Q E F T A Z W S A Select number route chromosomes t romly F S Pick best s prent route chromosome F 6 8 X 7. Tournment selection on popultion. 7. Tournment selection on popultion. E Finding solution 7 Fcse trvel route optimiztion F requires genetic lgorithm be set up in Finding specilized solution wy. For instnce, cse trvel vlid route solution optimiztion would need requires represent genetic lgorithm route where every be set 9 T loction up in specilized included wy. t lest, For instnce, only, once. vlid A solution route would would not need be considered represent vlid route if where it contins every single loction loction included more t thn lest, once, or only, 7. once. Tournment loction A route selection msed would on popultion. out not completely. be considered Hence, vlid specil if it contins types muttion single loction crossover more thn methods once, re or needed loction ensure msed genetic out completely. lgorithm Hence, does indeed specil meet types th Finding solution cse trvel route optimiztion requires genetic lgorithm be set requirement. muttion up in We crossover specilized utilize methods wy. order For crossover instnce, re needed vlid method solution ensure [35] would which genetic need ble represent lgorithm generte route does where vlid indeed every route. meet In th th crossover requirement. method, loction We utilize two included rom t order lest, cross crossover only, points once. method re A defined route [35] would which not subset be ble considered selected generte vlid if from it vlid contins route. first prent In th crossover n copied method, single loction in twomore rom thn once, sme positions crossor points loction re fspring. defined msed out Any completely. subset msing vlues selected Hence, specil re fromtypes determined first prent by muttion crossover methods re needed ensure genetic lgorithm does indeed meet th second n prent copied in only non repetitive sme positions genotype fspring. cn be dded Any msing fspring. vlues re determined 8 mde by so requirement. We utilize order crossover method [35] which ble generte vlid route. In th s second give prent little crossover clerer method, only n non-repetitive two explntion rom cross considering genotype points re defined cn be following dded subset exmple, selected fspring. from with two first prent rom 8 mde cross so points; s give 6 little 8. n Here, clerer copied in subset n explntion sme positions route considering tken fspring. from Any following msing first prent exmple, vlues re (6, determined with 7, 8) two by rom copied cross sme points; positions 6 second 8. Here, prent fspring. subset only non repetitive The routegenotype genotype tkencn fter from be dded second first cross prent fspring. point (6, 7, in 8) 8 mde second copied so s prent sme (1) give little clerer n explntion considering following exmple, with two rom cross not positions in fspring fspring. so tht it cn The be genotype dded t fter position second 9 cross fspring. point in Trversing second prent second (1) prent not in points; 6 8. Here, subset route tken from first prent (6, 7, 8) copied circulrly, fspring so sme we tht get positions it cn genotype be dded fspring. (9, t position 8, The 7, genotype 6, 5) 9 where fter fspring. 9 second 5 cn cross Trversing be point copied in second second fspring, prent prent (1) circulrly, skipping we get ors. Finlly, genotype fspring 4, (9, 3, 2 so 8, re tht 7, copied 6, it cn 5) where be dded 9 fspring t position 5 cn9 t be position copied fspring. 3, 4, Trversing fspring, 5, respectively. second skipping prent Th process ors. Finlly, repeted 4, 3, circulrly, until 2 re copied we get fspring genotype hs fspring (9, 8, 7, no more t6, position 5) where 9 empty vlues 3, 4, 5 cn 5, be respectively. copied fspring, end result Th should process skipping be complete repeted ors. Finlly, 4, 3, 2 re copied fspring t position 3, 4, 5, respectively. Th process route until which fspring contins hs ll no more positions empty vlues from its prents end with result no msing should be or duplicted complete positions. route which repeted until fspring hs no more empty vlues end result should be complete contins ll positions from its prents with no msing or duplicted positions. route which contins ll positions from its prents with no msing or duplicted positions. 8. Order on two prent routes. 8. Order crossover on two prent routes. 8. Order crossover on two prent routes. After crossover, muttion function pplied ech chromosomes output Afternew genertion. crossover, Muttion muttion n operr function used mintin pplied genetic ech diversity chromosomes from one genertion output popultion next. Muttion bsiclly modifies one or more genes in n individul from its new genertion. Muttion n operr used mintin genetic diversity from from one one genertion initil stte. The genetic lgorithm comes better solution s solution my chnge entirely popultion from previous next. next. Muttion solution by bsiclly using bsiclly muttion. modifies modifies However, onefor or one more trvel more genes route genes optimiztion in n individul n problem, individul from its from initil its initil stte. The stte. genetic muttion The genetic lgorithm method lgorithm should comes only be comes cpble better shuffling solution better solution s route without solution s ever solution my dding chnge or my removing entirely chnge from entirely from previous previous solution loction from by solution using route, by muttion. which using orwe muttion. However, might However, for rk creting trvel for route n invlid trvel optimiztion solution. route optimiztion Swp problem, function problem, [36] muttion one type pproprite method tht cn be used, which closest muttion operr in muttion method should method only should be cpble only be cpble shuffling shuffling route without route ever without dding ever or dding removing or removing loction philosophy originl muttion operr since it only shuffles route rr thn modifies loction from from route, which route, orwe which orwe might rk might creting rk creting n invlid n invlid solution. solution. Swp function Swp function [36] [36] one one type pproprite method tht cn be used, which closest muttion operr in philosophy originl muttion operr since it only shuffles route rr thn modifies

11 Processes 2018, 6, type pproprite method tht cn be used, which closest muttion operr in philosophy Processes originl 2018, muttion 6, x FOR PEER operr REVIEW since it only shuffles route rr thn modifies 11 originl 20 one. For exmple, we select two loctions from route t rom simply swp ir positions. The muttion Processes originl 2018, one. occurs 6, x For FOR exmple, inpeer user-definble REVIEW we select two probbility loctions from (in our cse route 1.5%), t rom which should simply be swp set11 low ir 20 s positions. The muttion occurs in user definble probbility (in our cse 1.5%), which should be set serch will turn in primitive rom serch when it set high. A more trnsprent explntion originl low s one. serch For exmple, will turn we in select primitive two loctions rom from serch when route it t rom set high. A simply more trnsprent swp ir presented in 9 by following exmple, where swp muttion pplied given route, positions. explntion The muttion presented occurs in in user definble 9 by following probbility exmple, (in our where cse swp 1.5%), muttion which should pplied be set here, new given route route, with here, new sme route vlues with creted sme vlues in different creted order in different by switching order by switching positions low s serch will turn in primitive rom serch when it set high. A more trnsprent 2 explntion 6. positions The proposed presented 2 muttion 6. The in proposed method 9 by muttion would following method never exmple, would cretewhere never route crete swp hving muttion route msing hving pplied or msing duplicte vlues or compred duplicte given route, vlues with here, compred new originl route with one with soriginl it only sme one swps vlues s it only creted pre-exting swps in different pre exting vlues, order which vlues, by switching exctly which wht trvel exctly positions route wht optimiztion 2 trvel 6. route The module proposed optimiztion muttion supposed module method do. supposed would never do. crete route hving msing or duplicte vlues compred with originl one s it only swps pre exting vlues, which exctly wht trvel Offspring route Route optimiztion Before Muttion module supposed do. Offspring 1 2 Route Before 3 Muttion Offspring Route After Muttion 1Offspring 6 Route After 3 Muttion Swp muttion on fspring route Swp 4 muttion 5 on 2 fspring 7 route. 8 9 The principle survivor selection 9. Swp [37] muttion tke on fspring good with route. bd, or in or words, The determine principle which individuls survivor should selection be kicked [37] out tkewhich good should with be kept in bd, next or in genertion. or words, determine Th The pper principle which employs individuls Elitm, survivor which should selection gurntees be[37] kicked out tke fittest member which good with should current be bd, kept or popultion in or next words, lwys genertion. Thdetermine pper propgted employs which individuls Elitm, next genertion which should gurntees be refore, kicked out under fittest which no member circumstnce should be cn kept current in fittest popultion next member genertion. lwys propgted Th current pper popultion employs next Elitm, be genertion replced. which We gurntees utilize refore, fitness bsed under fittest no member circumstnce selection, current where cn popultion fittest fspring member lwys tend replce lest fit individul in new popultion. As depicted in 10, generted current propgted popultion be next replced. genertion We utilize refore, fitness-bsed under no circumstnce selection, cn where fittest member fspring tend current fspring popultion replces be lest replced. fit individul We utilize P1 fitness bsed popultion in selection, originl where position. fspring The termintion tend replce lest fit individul in new popultion. As depicted in 10, generted fspring replce condition lest importnt fit individul s it determines new when popultion. sp running As depicted lgorithm. in 10, In th pper, generted replces termintion lest criterium fit individul defined P1 by using popultion n bsolute in number originl genertions position. The (in our termintion cse 1000) condition fspring replces lest fit individul P1 popultion in originl position. The termintion importnt condition thus lgorithm s importnt determines termintes s it when determines until sp counter when running reches sp running lgorithm. predetermined lgorithm. In count. th pper, In th pper, termintion criterium termintion defined criterium by using defined n by bsolute using n number bsolute number genertions genertions (in our(in cse our 1000) cse 1000) thus lgorithm thus Fitness termintes lgorithm until termintes Route counter until reches counter reches predetermined predetermined count. Fitness count. Route Vlue Chromosome Vlue Chromosome Fitness 1 Vlue Route P1 Chromosome P2 P1 P3 P2 P4 P3 P5 P4 P6 P5 P7 P6 P8 P7 P9 P8 Previous P9 Popultion Fitness Vlue Fitness 8 Vlue 8 Route Chromosome Route C1 Chromosome Offspring C1 Route Offspring Route Fitness 8 Vlue Route C1 Chromosome P2 C1 P3 P2 P4 P3 P5 P4 P6 P5 P7 P6 P8 P7 P9 P8 New Popultion P9 Previous Popultion 10. Fitness bsed selection for generting new popultion. New Popultion Fitness bsed selection for for generting new new popultion. popultion.

12 Processes Processes 2018, 2018, 6, 6, 133 x FOR PEER REVIEW Dtset Experiment 4. Dtset Experiment Th section represents dtset tht hs been used for experiment reports results. Th section represents dtset tht hs been used for experiment reports results. We use open dt which collected trvel informtion from 36,548 urts who hve been We use open dt which collected trvel informtion from 36,548 urts who hve been Jeju in The Open Dt Portl [38] releses th open dt public, covering more thn 130 Jeju in The Open Dt Portl [38] releses th open dt public, covering more thn competitive destintions throughout Jeju vited by se urts. It includes moving pth records 130 competitive destintions throughout Jeju vited by se urts. It includes moving pth records from ech urt in specific month yer. Ech pth contins strting destintion points from ech urt in specific month yer. Ech pth contins strting destintion points long with or trvel spots between two points. In generl, recommendtion provided by long with or trvel spots between two points. In generl, recommendtion provided by trvel recommender system cn be quite different with sesons. For exmple, urt plesed trvel recommender system cn be quite different with sesons. For exmple, urt plesed vit bech in summer, but not so much in winter, when wer unplesnt due cold vit bech in summer, but not so much in winter, when wer unplesnt due cold climtes. The extrcted route set presented in 11 contins vrious routes, which composed climtes. The extrcted route set presented in 11 contins vrious routes, which composed moving pth some or fetures; for exmple, consider route t1 s shown in moving pth some or fetures; for exmple, consider route t1 s shown in following following smple, contining user Id, yer, month moving pth three loctions (, b, c). smple, contining user Id, yer, month moving pth three loctions (, b, c). In keeping In keeping with sesonlity requirements recommender system, we ssume tht single with sesonlity requirements recommender system, we ssume tht single seson feture seson feture specified in dvnce. Th feture cn be esily clssified ccording month specified in dvnce. Th feture cn be esily clssified ccording month in current in current yer (i.e., December represents winter August represents summer) thus yer (i.e., December represents winter August represents summer) thus route set cn be route set cn be ctegorized in four subsets. ctegorized in four subsets. Tourt 1 Tourt 2 Tourt 3... Tourt n Route Extrction (one yer period) Id Yer Month Moving Pth Seson t >b->c Winter Route Sets t2 t >b c->d->e... Autumn Summer tn c->e->f->g Spring 11. smple extrcted route set from dtset. 11. A smple extrcted route set from dtset. Tble 1 reports experimentl results ssocition rule bsed recommendtion without considering Tble 1 sesonlity. reports experimentl For th nlys, results eight cses ssocition with different rule bsed minimum recommendtion support vlues without from 0.03 considering 0.1 re sesonlity. performed For by th nlys, proposed eight system cses ten with times different t romly minimum selected support system vlues resource from utiliztion re levels. performed In th pper, by proposed min support system ten specified times in t percentge romly selected insted system single resource count due utiliztion levels. lrge volume In th pper, dt. We min set support minimum specified confidence in percentge constnt insted (0.9) single which count indictes due high lrge strength volume rules dt. generted. We set It cn minimum be seen from confidence tble tht constnt number (0.9) rules which decresed indictes with high increment strength rules minimum generted. support It cn vlue. be seen The from number tble best tht rules number 125 when rules min support decresed 0.03 with while increment number rules minimum reduced support 29 when vlue. min The support number 0.1. best Moreover, rules 125 when minimum min support support hs gret 0.03 while effect on number number rules cycles reduced which determines 29 when how minlong support it tkes 0.1. run. Moreover, For exmple, minimum verge support run hs time gret tken effect recorded on number cycles s when which determines minimum support how long 0.03 it tkes decresed run. For exmple, by 39.4% when verge minimum run timesupport tken recorded be s when minimum support 0.03 decresed by 39.4% when minimum support 0.04.

13 Processes 2018, 6, Tble 1. Trvel route recommendtion performnce evlution. Min Supp Min Conf No. Cycles No. Best Rules Averge Run Time s s s s s s s s Tble 2 presents nor experiment test considering sesonl fcrs, in which originl dtset seprted in four subsets ccording sesons. In th test, ll cses re performed by proposed system ten times t romly selected system resource utiliztion levels under sme conditions, where minimum support 0.07 minimum confidence set 0.9. It cler see tht recommender system genertes 125 rules for utumn, which most in ll sesons. The min reson tht most urts prefer go sightseeing in utumn since climte cooler more comfortble thn or sesons. Spring next fter utumn with 61 rules, summer third with 29 rules winter lest with only 13 rules. Tble 2. Trvel route recommendtion performnce evlution in different sesons. Seson Min Supp Min Conf No. Cycles No. Best Rules Averge Run Time Spring s Summer s Autumn s Winter s A smple lt recommended routes ccording sesons presented, s shown in Tble 3. It cn be seen from tble tht recommendtion contins more outdoor venues in spring thn in summer, which resonble due hot climte, people re more likely sty indoors. It obvious see tht se routes contin sme loctions, but in different order. For exmple, we cn generte 6 different routes in cse 3 loctions which cn be simply clculted by fcril 3. However, if number loctions in route grows, fcril will be gigntic, which mkes it lmost imprcticl find shortest route mong such huge number different routes. Tble 3. A smple recommendtion in spring summer. Seson Id Moving Pth Spring Summer 1 Jeongbng Flls, Chilshimni Food Street, Seobok Exhibition Hll, Jungmun Tourt Complex 2 Seobok Exhibition Hll, Jeongbng Flls, Chilshimni Food Street, Jungmun Tourt Complex 3 Chhimni Food Street, Jeongbng Flls, Seobok Exhibition Hll, Jungmun Tourt Complex 1 Ply K-Pop Jeju, Teddy Ber Museum, Pcific L, Cheonjeyeon Flls 2 Teddy Ber Museum, Cheonjeyeon Flls, Pcific L, Ply K-Pop Jeju 3 Cheonjeyeon Flls, Pcific L, Ply K-Pop Jeju, Teddy Ber Museum In th pper, we use genetic lgorithm s optimiztion technique which cpble dcovering optiml solution th problem. In order demonstrte performnce proposed route optimiztion pproch, we pply test gend with four instnces s illustrted in Tble 4. Ech instnce contins lt plces. The vlues some relevnt prmeters for genetic lgorithms re defined s follows: popultion size set 100, mx genertion set 1000, muttion rte set t 1.5%.

14 Processes 2018, 6, Processes 2018, 6, x FOR PEER REVIEW Tble 4. Test gend for trvel route optimiztion No No. Plces Tble 4. Popultion Test gend Size for trvel Mx route Genertion optimiztion. Muttion Rte No 1 No. Plces 10 Popultion 100 Size Mx Genertion 1000 Muttion 1.5% Rte % 1.5% % % % % Selection Type: Tournment Selection; Crossover Type: Order Crossover; Muttion Type: Swp Muttion % Selection Type: Tournment Selection; Crossover Type: Order Crossover; Muttion Type: Swp Muttion. The performnce hs been nlyzed results re reported in Tble 5. For th nlys, four route The sets performnce 10, 25, 40, hs 55 been plces nlyzed re provided results proposed re reported routein optimiztion Tble 5. For module. th nlys, Ech four setsroute performed sets 10, 20 25, times 40, 55 independently plces re provided t romly proposed selectedroute system optimiztion resource utiliztion module. Ech levels. The best sets result, performed verge result, 20 times independently optimum vluet re romly recorded. selected For system set with resource 10 plces, utiliztion best result levels. recorded The best result, be 789, verge verging result, t optimum 801 vlue optimum re recorded For For set set with with 1025 plces, plces, best best result recorded be be 1550, 789, verging t optimum For Similrly, set with 25 test results plces, for best rest result instnces recorded re recorded, be 1550, respectively, verging t ll 1620 se results optimum re vully demonstrted Similrly, in test 12. results for rest instnces re recorded, respectively, ll se results re vully demonstrted in 12. Tble 5. Test results for ech test instnce. Tble 5. Test results for ech test instnce. No No. Plces Best Result Averge Result Optimum No No. Plces Best Result Averge Result Optimum Test Results for Ech Instnce Totl Dtnce (km) No Test Instnce Best Result Averge Result Optimum 12. Performnce nlys grph different test instnces. 12. Performnce nlys grph different test instnces. 5. Implementtion Detils 5. Implementtion Detils Th section illustrtes development ols, hrdwre, technologies used in implementtion. Th section illustrtes The proposed development work compres ols, hrdwre, two min components technologies s shown used in in implementtion. 1 so tht The proposed development workstcks compres re independently two min components summrized s in shown two tbles in for ech 1 so tht components. development stcks re Tble independently 6 depicts summrized development inolkits two tbles for technologies ech components. used for optiml trvel route recommender system. The implementtion performed on development mchine with Intel(R)

15 Processes 2018, 6, Tble 6 depicts development olkits technologies used for optiml trvel route recommender system. The implementtion performed on development mchine with Intel(R) Core i CPU t 3.30 GHz, 12 GB memory Windows 7 Ultimte 64 bits operting system. The implementtion pltform Eclipse Lun IDE using Jv progrmming lnguge. The dtset used formed in CSV formt so tht opencsv used, which Jv specified CSV prser librry for reding or writing CSV files. Tble 6. Development environment optiml trvel route recommender system. Component Chrcteriztion Operting System Windows 7 Ultimte 64 bits CPU Intel(R) Core i Memory 12 GB IDE Eclipse Lun (4.4.2) Librry Frmework opencsv Progrmming Lnguge Jv Tble 7 introduces development stck for developing mobile urt ppliction. Th mobile ppliction built in Jv XML progrmming lnguges using Android Studio IDE. SQlite 3 widespred dtbse engine used for n embedded system such s mobile phones. It used sre routes generted from recommender system. Dum MAP API provides vrious functionlities cusmize mps with users own content imgery for dply on mobile devices. YouTube Dt API llows user dd vriety YouTube fetures ppliction, for instnce, it supports functionlities serch for videos mtching specific serch terms, pics, loctions. The km API cptures rel-time wer dt for ny loction including wind, temperture, humidity, more. Th ppliction tested in Glxy S4 with Oct-core (8-core CPU), 2GB memory Android Component Tble 7. Development stck mobile urt ppliction. Chrcteriztion Model Nme Glxy S4 SHV-E300K Operting System Android CPU Oct-core (4 1.6 GHz Cortex-A15 & GHz Cortex-A7) Memory 2 GB IDE Android Studio DBMS SQlite3 Librry Frmework Dum Mp API, km API, YouTube Dt API Progrmming Lnguge Jv, XML 6. Use Cse Study: Mobile Tourt In order demonstrte usbility proposed system, mobile urt cse study hs been implemented s prt experiment. Th ppliction ingests trvel routes generted from trvel route recommender system se routes re sred in locl dtbse for furr processing vrious services. The min interfce s shown in 13 provides n overview th ppliction, which minly shows mp Jeju. The rel-time wer informtion dplyed in different directions on mp. There re lso severl options t botm ppliction, which provide entries for different function interfces.

16 Processes 2018, 6, Processes 2018, 6, x FOR PEER REVIEW Processes 2018, 6, x FOR PEER REVIEW Mobile urt ppliction min interfce. 13. Mobile urt ppliction min interfce. 13. Mobile urt ppliction min interfce. The min function th ppliction provide trvel routes depending on strt point The min function th ppliction trvelroutes routesdepending dependingonon strt point function thpresents ppliction provide provide trvel strt enteredthe by min user. 14 interfce route serching, in which users cn point retrieve entered byby user presents interfce route user. interfce routeserching, serching,ininwhich whichusers userscn cnretrieve retrieve ll llentered vilble routes or serch presents specific route by keywords. vilble routes or serch specific route by keywords. ll vilble routes or serch specific route by keywords. () () (b) (b) () ()trvel trvelroute route lt lt form; form; (b) routeusing usingkeywords. keywords. (b) serch serchroute 15 shows some snpshots vrious interfces for trvel routes spots shows showssome somesnpshots snpshots vrious interfces for trvel routes spots. 15 vrious interfces for trvel routes spots. 15 shows shows overview trvel routes locted on mp long with complete route informtion shows overview trvellocted routes on locted on mp long complete route informtion overview trvelroutes mp long with with complete route informtion which which dplyed t botm mp. Ech trvel spot from route lbeled with blloon which dplyed t botm mp. Ech trvel spot from route lbeled with blloon dplyed it botm mp. Ech trvel spot from route lbeled with blloon mrker t necessry notice tht blloon mrker in red represents currentmrker loction mrker itnotice necessry blloon notice tht in blloon mrker in red represents current loction it while necessry tht mrker red represents current loction while mrker mrker in blue sts for next destintion route. Furrmore, mp lso in while mrker inbove blue sts for next destintion route. Furrmore, mp lso blue sts next destintion Furrmore, lso renders bubble renders for bubble mrker thtroute. dplys nme mp given spot. By clicking bove given renders bubble bove mrker tht dplys nme given spot. By clicking given mrker thtth dplys nme given spot.interfce By clicking given mrker, th ppliction mrker, ppliction redirects nor s represented in 15b, where redirects urm mrker, th ppliction redirects nor interfce s represented in 15b, where urm nor interfce s represented in 15b, where urm informtion selected trvel spot informtion selected trvel spot detiled. 15c presents interfce for dplying informtion selected trvel spot detiled. 15c presents interfce for dplying detiled. 15c presents interfce for dplying relted introducry videos trvel spot relted introducry videos trvel spot from YouTube. relted introducry videos trvel spot from YouTube. from YouTube.

17 Processes 2018, 6, Processes 2018, 6, x FOR PEER REVIEW () (b) (c) 15. () trvel route with mrkers; (b) (b) trvel spot detiled info; (c) trvel spot introducry video. 7. Compron Significnce Th section provides comprtive nlys proposed trvel route recommender system with some similr works mentioned in Relted Work Section. A benchmrk study ws crried out using following fetures revel superiority pplicbility proposed work evlution results re shown in Tble It It cn be seen from tble tht proposed system performs better thn or systemsin in mny respects. Most Most systems systems only only provide provide n n individul individul recommendtion, recommendtion, neir neir utilize utilize personl personl ttributes ttributes nor supports nor supports route genertion, route genertion, which which gretly gretly reduces reduces conveniences conveniences for users. forthe users. work The [24] work [24] somewht somewht similr pproch similr pproch with with highest highest vriety vriety trvel trvel recommendtion recommendtion relted relted services. services. Th Th system system utilizes personl ttributes-bsed ttributes bsed mining pproch generte trvel routes shows recommendtion results in smrtphones. However, route optimiztion not considered which might cuse recommendtion be be not not resonble. Th Th limittion limittion common common sue sue tht exted tht exted mong mong most most current systems. current Moreover, systems. Moreover, mny systems mny dosystems not support do not mobile support pplictions, mobile which pplictions, nor which mjor limittion. nor mjor limittion. Tble 8. Comprtive nlys proposed system with relted systems. Tble 8. Comprtive nlys proposed system with relted systems. Personl Route Dt Route Mobile Nme Personl Route Dt Route Mobile Nme Attributes Genertion Sre Optimiztion Support Attributes Genertion Sre Optimiztion Support [12] No No Yes No No [12][13] No No No No Yes Yes No No No No Gore [13] [14] No No No Yes No No No No Gore [16] [14] No No No Yes No No No No [16][17] No No No Yes No No Yes No [19] No Yes Yes No No [17] No No Yes No Yes [20] No Yes Yes No Yes [19] W2go [21] No No Yes Yes No No No No [20][22] No Yes Yes Yes No No No Yes W2go [21] [23] No Yes Yes Yes No No Yes No Proposed System Yes Yes Yes Yes Yes [22] Yes Yes Yes No No [23] Yes Yes Yes No Yes Th Proposed work proposes System rel-life Yes cse study Yes for urts Yes in Jeju Yes verify fesibility Yes prcticbility proposed pproch. The trvel urm industry huge it ws estimted beth 6.3 work trillionproposes dollr industry rel life in cse The study impct for urts proposed in Jeju solution verify cn be fesibility summrized s prcticbility follows: first, it cn proposed chngepproch. conventionl The trvel urm urm industry industry since urts huge do not it ws needestimted check be 6.3 trillion dollr industry in The impct proposed solution cn be summrized s follows: first, it cn chnge conventionl urm industry since urts do not need check

18 Processes 2018, 6, guidebooks or periodicls for plnning ir upcoming urs. Nowdys, more thn 150 million urts use smrtphones, for th reson, y cn use mobile urt ppliction with just single click. Moreover, proposed solution cn gretly ffect on-site trvel behvior serve s bs for future enhncements in urm industry since it provides personlized services by nlyzing trvelers experience during ir trip. We hold opinion tht designed system hs potentil be exped in or scenrios such s hotel resturnt recommendtion, which cn dditionlly benefit from significnce work. Furrmore, dems hve relible user-friendly trvel guide ppliction tht fers personl ttributes mining, trvel route genertion, trvel route optimiztion re growing rpidly, th pper ims revel potentil solve ll se sues mentioned bove. 8. Conclusions Future Direction Th work outlines novel procedure for design implementtion trvel route recommender system help id in both trip plnning s well s instntneous trvel informtion. An ssocition rule mining-bsed pproch which considers personl trvel ttributes proposed produce trvel route including vrious destintions. The biggest innovtion presented work utiliztion genetic lgorithm optimize trvel route, by referring dtnce fitness function in order find optiml route. A series experiments re performed using open ccess trvel dt in Jeju from Jnury December 2017, permitting efficient relible route recommendtion optimiztion ccording user personl ttributes. Furrmore, mobile urt ppliction hs been implemented s pro concept experimentlly tested in Jeju, Kore vlidte performnce proposed pproch. The significnce th work hs been highlighted by comprtive nlys designed system with exting pproches, result demonstrtes tht designed system outperforms or systems in mny spects. It gol th work fill gps in mobile-bsed trvel plnning pplictions, mke life urt esier, ccelerte urm industry developments. Moreover, proposed work cn be exped mny or ppliction scenrios such s hotel reservtions cr rentls on bs originl ppliction. Future reserch directions include deploying designed ppliction in lrger ppliction domin for ll kind trvel purposes while using much wider rnge dtsets tht cover whole Kore. Author Contributions: L.H. conceived ide for th pper, designed experiments wrote pper; S.-H.K. implemented mobile urt ppliction for use cse study; W.J. fered dtset ory support; D.-H.K. conceived overll ide trvel route recommendtion for mobile urts, pro-red mnuscript, ws correspondence relted th pper. Funding: Th reserch ws supported by MSIT (Mintry Science ICT), Kore, under ITRC (Informtion Technology Reserch Center) support progrm (IITP ) superved by IITP (Institute for Informtion & communictions Technology Promotion), th reserch (pper) ws performed for Development Rdr Pylod Technologies for Compct Stellite in Kore Aerospce Reserch Institute, funded by Mintry Science ICT. Any correspondence relted th pper should be ddressed DoHyeun Kim; kimdh@jejunu.c.kr. Conflicts Interest: The uthors declre no conflict interest. References 1. TripSpot. Avilble online: (ccessed on 8 Jnury 2018). 2. Sng, J.; Mei, T.; Sun, T.J.; Li, S.; Xu, C. Probbiltic sequentil POIs recommendtion vi check-in dt. In Proceedings ACM SIGSPATIAL Interntionl Conference on Advnces in Geogrphic Informtion Systems, Redondo Bech, CA, USA, 6 9 November 2012; pp Bo, J.; Zheng, Y.; Wilkie, D.; Mokbel, M. Recommendtions in loction-bsed socil networks: A survey. Geoinformtic 2015, 19, [CrossRef]

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