A SOCIAL ENDORSING MECHANISM FOR LOCATION-BASED ADVERTISING

Transcription

1 Assocaton for Informaton Systems AIS Electronc Lbrary (AISeL) PACIS 2014 Proceedngs Pacfc Asa Conference on Informaton Systems (PACIS) 2014 A SOCIAL ENDORSING MECHANISM FOR LOCATION-BASED ADVERTISING Len-Fa Ln Kao Yuan Unversty, lenfa0704@gmal.com Yung-Mng L Natonal Chao Tung Unversty, Tawan, yml@mal.nctu.edu.tw Follow ths and addtonal works at: Recommended Ctaton Ln, Len-Fa and L, Yung-Mng, "A SOCIAL ENDORSING MECHANISM FOR LOCATION-BASED ADVERTISING" (2014). PACIS 2014 Proceedngs Ths materal s brought to you by the Pacfc Asa Conference on Informaton Systems (PACIS) at AIS Electronc Lbrary (AISeL). It has been accepted for ncluson n PACIS 2014 Proceedngs by an authorzed admnstrator of AIS Electronc Lbrary (AISeL). For more nformaton, please contact elbrary@asnet.org.

2 A SOCIAL ENDORSING MECHANISM FOR LOCATION-BASED ADVERTISING Len-Fa Ln, Department of Informaton Communcaton, Kao Yuan Unversty, Kaohsung, Tawan, R.O.C., Yung-Mng L, Insttute of Informaton Management, Natonal Chao Tung Unversty, Hsnchu, 300, Tawan, Abstract The prolferaton of smartphones explots many new opportuntes for moble advertsng. Many researchers beleve that moble advertsng wll be not only a kller applcaton n moble commerce, but also a noteworthy busness model for many emergng moble applcatons to monetze. Locaton based advertsng (LBA) s a new advertsng that ntegrates moble advertsng wth locaton based servces. Snce LBA s related to the locaton of smartphone users, we analyze the movng trajectory of smartphone user and then try to fnd out the stable endorsers for dssemnate advertsng. In ths paper, a moble socal advertsng mechansm, whch consders the factors of preference, locaton of servce, movng trajectores of smartphone users, and the nfluence power of endorser, s proposed to enhance locaton based commerce. When the targeted users for locaton based servce s dentfed, the proposed mechansm can dscover the most approprate endorsers, whch can dssemnate the ads to the target users at a stable locaton. Accordng to the evaluaton model, locaton based servce provders know that how to choose the endorsers for dssemnatng advertsng messages so as to attract the most smartphone users. Keywords: Moble Commerce, Socal Advertsng, Locaton Based Servce, Socal Analyss..

3 1 INTRODUCTION Wth the rapd rse of smartphone developments and onlne technologcal advances has brought about a burst n new forms of advertsng (Nelsen report 2012). Moble advertsng s becomng a new marketng channel and has the capablty to reach mllons of smart moble devces at the rght tme, the rght place, and to the rght consumer (Aaker & Stayman 1990). Along wth the emergence of socal meda, locaton-based servce and moble devce, John Doerr (2013) proposed a concept named SoLoMo (Socal-Local-Moble), takng the form of socal networkng servce (e.g. Facebook, Twtter, and Foursquare) combned wth moble phone platform and postonng servce (e.g. check-n and navgaton), to envson a new paradgm of moble commerce. Many new busness applcatons are njected wth the concept of SoLoMo. For example, marketers can explore potental customers by analyzng ther behavors on the socal network platform and current locaton to make personalzed recommendaton of POI (pont-of-nterest), coupons or ads (L & Du 2012) through the moble channel to ncrease busness opportuntes. Wth the applcatons empowered wth socal and moblty ntellgence, venders can dscover potental customers wth hgh possblty of buyng merchandse and the customer can also beneft from reducng the cost of fndng the stores that st ther needs. Although ths new paradgm, t has a rsng trend. However, t s stll a bg ssue faced by enterprse about how to apply moble advertsng to effectvely create value n busness. Accordng the report by Mary Meeker n 2012 Internet Trends D10 Conference (Mary Meeker s 2012), moble devces account 10% of the tme spent on meda,but to our surprse, the money spend n moble Ad s only account for 1% of advertsng spendng,usa The average revenue per user (ARPU) also remans far behnd on moble. One of the prmary benefts of moble advertsng s the potental to nstantly lnk wth consumers wherever they are postoned. Locaton-based servces (LBS) appeal enormous attentons due to ther potental to transform moble communcatons va realzng a varety of hghly personalzed and context-aware servces (Menon & Horney 2009). How to provde ntegrated socal and moble meda s attractng ncreasng attentons from academcs and practtoners. The tradtonal moble advertsng mostly reles on geographc locaton based moble advertsng. The ads are dssemnated smply based on the nformaton of user s locaton. It s nsuffcent to meet the users need, whch changes wth dynamc contexts. In ths paper, a moble socal advertsng mechansm, whch consders the factors of preference, locaton of servce, movng trajectores of smart phone users, and the nfluence power of endorsers, s proposed to enhance the effectveness of locaton based advertsng (LBA). The proposed mechansm can feasbly dstrbute varous types of LBS ads by consderng the followng three aspects. Frst, from the targetng aspect, the stable recevers could be found based on the contents and characterstcs of ndvdual nformaton and the servce range constraned of LBS. The ftness degree between the ads and the recevers s evaluated before the advertsng actvtes launched. Second, from the ad agences aspect, the ad agency wth hgh reputaton and socal nfluence s a powerful ad dstrbutng strategy to reduce the negatve reacton durng advertsng. Especally, when the endorsers wth hgh nfluence to the recevers are selected, the ad wll be more lkely acceptable to the recevers. It helps to overcome the ssues of the advertsng avodance. Ths helps the nformaton propagaton of the ad and reduces the occurrence of advertsng avodance. Thrd, from the syntheszng aspect, the proposed mechansm combnes both the power of target and socal advertsng. When the targeted recevers are dentfed and clustered by the preference analyss, the system could further fnd out the set of most nfluental endorsers to delver the advertsng messages. Flterng out the targeted users helps to scale down the scope of advertsng and dscover the advertsng agency effectvely. The marketers can meet ther expected outcome based on the objectve of advertsng by utlzng ths syntheszed target and socal advertsng strategy. The remanng sectons are organzed as follows. Secton 2 dscusses the related lterature. In Secton 3, the research model wll be demonstrated, and the experments wll be presented n Secton 4. The

4 experment results and evaluaton are dscussed n Secton 5. Fnally, Secton 6 concludes ths study and presents the drectons of future research. 2 RELATED WORK 2.1 Locaton-Based Servces Locaton-based servces (LBS) are a class of a computer program servce, ncludng the specfc poston and tme data as control characterstc n computer programs, whch s accessble wth moble devces and whch uses knowledge on the geographcal poston of the moble devce. Ths servce has become ncreasngly promnent wth the growng of the market of smart phones and tablets. How to utlze smart moble devce and LBS to ncrease the sale volume s becomng a strkng ssue among the e-commerce market. The global locaton-based servces market s a delght n hgh growth. Revenue forecasted reaches US $10.3bn n 2015, from $2.8bn n 2010 (Sythoff & Morrson 2011). A number of dfferent factors drvng market growth, such as ncreasng GPS and smartphone adopton, success of new busness models, persstent growth of moble advertsng, and the broader coverage and hgher speeds of wreless moble networks. However, wth the growth of LBS market, there are problems of low acceptance of LBS. Hence, how to meet a consumer s need and attract more customers to use LBSs s the key ssue. In ths paper, we motvate on how to mprove the acceptance of LBS wth socal advertsng mechansm by explotng socal, locaton, and moblty data, whch can also rase the revenue of LBS. 2.2 Onlne Advertsng There are three onlne advertsng paradgms whch have been popularly mplemented: target advertsng, socal advertsng, and locaton-based advertsng. Target advertsng focuses on dentfyng the rght (targeted) recevers of the ad. By specalzng and segmentng the market, the precse specfc customers can be dentfed (L & Du 2012). However, even the target advertsng approach can effectvely dentfy the end audence, an effectve dstrbuton channel s reqred to ensure the success of marketng. For example, when target advertsng s appled n the form of drect marketng, the audence would feel offended and refuse to accept the ads delvered by the advertser or the frm. Ths dsmsses the effectveness of the ad due to ts napproprate dstrbutor. Forllo (2009) notes that socal advertsng can dstrbute approprate ads through the frend network of users. Bagherjeran and Parekh (2008) pont out that the socal relatons and socal nteractons between users are the crtcal factors n realzng actvtes of socal advertsng. The moble advertsng market posed for massve growth as t contnues to explot some appealng features of moble devces, such as portablty, personalzaton and nstant access, moblty and wreless nternet connectvty, context-aware and locaton-aware. All these appealng features coupled wth useful applcatons have ncreased the adopton rate of these devces and as a consequence of these usages of these devces are growng fast. Therefore, t makes busness sense to use the moble devces as new platform for advertsng, whch can be customzed based on the user s preferences and geo-poston. The users can also select the knds of ads they want to receve on ther smart phone. Ths nformaton would be kept on a server, and the ad would be sent accordngly. Durng the frst tme set up, the subscrbers wll have the chance to provde ther preferences for the knds of ads to be receved and dsplayed on hs moble devce. In ths paper, we also utlze these three factors to desgn the LBS advertsng strategy and develop a socal advertsng mechansm to enhance the value of LBS advertsng. 2.3 Endorser Marketng Marketng wth endorsers s a common and useful strategy. The person who can appear ths nfluence on an ndvdual or group s called the endorser (McCracken 1989). Kss and Bchler (2008) wdely revew the general centralty measures for selectng nfluencers/endorsers from an endorser network

5 for onlne marketng. An endorsed advertsng message can affect the behavors of customers. A celebrty endorser s a person who owns publc recognton and uses ths recognton on behalf of a consumer good by appearng wth t n an ad. In other words, a celebrty endorser has a sgnfcant nfluence to affect people on behavors. The advertsng researchers note that the postve emotonal atttude s promnent on the goal of advertsng and can be vewed as an ndcator on advertsng effectveness (Aaker & Stayman 1990). Lee et al. (2003) also ndcate that a greater pleasure and arousal emoton sgnfcantly led consumers to have a more postve atttude n the Internet shoppng mall. Ths suggests that the endorsement strategy, especally the celebrty endorser approach, s helpful to conduct advertsng and can be expected wth better effectveness.snce the endorsers can nfluence the behavors of customers, the endorsement mechansm would be extremely helpful on advertsng. In ths paper, we adopt the concept of endorser marketng to develop a socal advertsng mechansm for LBSs to enhance the value of the ad receved. 3 THE SYSTEM FRAMEWORK To mprove the postve mpressons through a successful moble socal advertsng, we should frst fnd out the rght target audences to receve an LBS ad, and then explot the socal nfluence and trajectory analyss approach to dentfy the rght locaton of endorsers to dssemnate the LBS ad to do so. These ads would be spread wthn a vald LBS servce scope (Lee et al. 2010) and realze the sprt of dssemnatng the rght ad to the rght people va the rght endorsers at rght place. Fgure 1 depcts the system framework of the proposed system. 1. The system dentfes targeted customers accordng to the product, locaton and promoton characterstcs of the LBS that the advertser s plannng to dstrbute. 2. The system constructs a network of canddate endorsers located at the vald servce scope of the LBS. 3. The system dscovers the most approprate endorsers from the network of canddate endorsers. These seed endorsers should be delegated to dssemnate LBS ad to the target smart phone users (SPU) that they wll move across the vald servce scope of the LBS servce provder. In the context of tradtonal servce cell wthout dssemnatng LBS ad, the target users just move n accordance wth the establshed path of habtual. However, t cannot make the most of the target users to use the LBS. Wth the help of the socal advertsng nowadays, the most target users of LBS can be attracted and turn ther movng drecton to the servce provder. Fgure 2 llustrates ths scenaro. To match the objectve of attractng the smart phone target users to turn ther movng drecton to the locaton of LBS servce provder, several technques are reqred. The man components consdered n the system framework consst of the target dscoverng mechansm, the endorser canddate network constructng mechansm, and the seed endorser dentfyng mechansm. The system framework s depcted n Fgure Target dscoverng mechansm The target dscoverng mechansm analyses the ftness degree between the preferences of users and the servce type of LBS. Measurng the smlarty between the categores of ther preferences and the category of the LBS wll dscover target users. In addton, because the servce s locaton dependent, only those customers close to the vald servce scope can be the fnally target users Preference Analyss Module Because the property of a user group s consdered as a vtal factor, usng preference analyss technques to dentfy target users s essental. Ths module s desgned to analyze the preferences of

6 users and categorze them. The basc measurement s to analyze the profles and the posts of users. It helps to fnd the keywords representng the key characterstcs and tendences of users. The statc preferences of users are the nformaton obvously dscovered from ther profles. Snce the profles data wll not be updated frequently, t s vewed as statc preference. The data can be gathered from self-revealed nformaton. Fgure 1. System framework of moble socal advertsng (a) Wthout advertsng (b)wth advertsng Fgure 2. Effect of the socal advertsng Fgure 3. Archtecture of the targeted socal advertsng mechansm The nterests of users are not always dentcal all the tme. In general, the user profles wll not be updated regularly. Ths makes t dffcult to fnd the changes n ther nterests from ther profles. However, ther changes n nterests could be learned from ther recent conversatons. What they menton n ther latest conversaton mght reveal ther new concentratons and nterests. The nformaton revealed from users recently posts s defned as dynamc preference. The dynamc preferences are used to support and expand the statc preferences dscovered from user profles. Consstent wth frequently used the keywords dscovered from the messages of nteractons, we can categorze them by matchng the keywords descrbed n the LBS servce type developed n subsecton As stated by the preferences of users, they can be classfed nto dfferent categores.

7 3.1.2 Ad Attrbute Analyss Module The objectve of ths module s to poston a gven ad. The target users are dscovered and dentfed by analysng the smlarty between the users preference categores and the ad s category. To evaluate the ftness between an ad and users preferences, a tree-lke structure s adopted n ths study. There are several pror studes used the same structure, partcularly n the felds of product taxonomy (Zegler et al., 2004) and semantc smlarty n taxonomy (Resnk 1999). We use the dstance-based approach, whch has been shown better than other keyword-based smlarty estmate approaches (Yuan & Cheng 2004), to evaluate the smlarty between an ad and a target user s preference. Assume user u s allocated under category C u and ad a s under the category C a. The C m presents the frst mutual node that C u and C ad has. So the dstance from C u to C m and C a to C m wll be used to measure ther smlarty based on the category tree. Ths smlarty degree s formulated as: Sm( C, C u 2Drm a), (1) Dum Dam 2Drm The dstance from node C u and C a to the node C m s denoted as the D um and D am. And the length of the path from C m to the root node n the category tree s represent as D rm. As a user may have multple preferences, we can further compute the average ftness score by the followng formula. 1 Ftness( u, LBS) Cate( u) Cu Cate( u) Sm( C, Cate( LBS)), (2) u Cate(X) denotes that X s categorzed nto whch category. Cate(X ) represents all preference categores of X. By the ftness computaton formula, we can fnd the set of targeted customers who have nterest n the advertsng message the sponsor wll dstrbute. We denote the set of target customers as TU. TU { u Ftness( u, LBS) }, where LBS s the servce type promoted by LBS servce provder and ε s the threshold for ftness acceptance level Vald Servce Scope Analyss Module Due to locaton-dependent servce s only servced at specfc locaton. Hence, even the smart phones users have receved a very attractve ad for LBS, f the dstance s far away from the smart phone users to LBS, the smart phone users can t drve to the LBS destnaton and use ths servce. To ensure havng the postve effect for moble advertsng, n ths paper, a vald servce scope s gven for LBS. The objectve of ths module s to fnd the customers who lve nearby the locaton or habtually move across the servce range of LBS. The measurement of ths module s denoted as GF score that represents the degree of geographc ftness for a customer to use the LBS. The processes of locaton dependent nformaton collecton bounded wth the LBS are explaned as follows. The locaton data s obtaned drectly from the user profle recorded n the moble socal network platform to fnd out the customers who lves or has ever lved n the cty where the LBS locate. The locaton nformaton of a customer, such as company address and current cty he/she lves, can be obtaned from hs/her profle. The statc locaton vector of customer s represented as SL( u )= [company( ),cty( )]. If LBS j provdng the servce s located n cty L(LBS j ), the statc locaton score for customer wth respect to LBS j s measured as formula (3). 1f companyu ( ) L( LBS j ) and cty( ) L( LBS j ); a f companyu ( ) L( LBS j ) and cty( ) L( LBS j ); Statc(, LBS j ) b f companyu ( ) L( LBS j ) and cty( ) L( LBS j ); 0 f companyu ( ) L( LBS j ) and cty( ) L( LBS j );, (3)

8 where0 a b 1. As well as statc profle nformaton, some hdden nformaton from the customer actvtes on the onlne socal network platform could be gathered as well. Numerous socal networkng servces, such as Foursquare, Whrrl and Facebook allow users to check n to a physcal place and share ther locaton wth ther frends. Users can ssue a message of check-n nformaton to a specfc locaton va text messagng or by usng a moble applcaton on a smartphone. In ths paper, check-n nformaton s used to analyse user s actvty locaton nformaton. In general, check-n nformaton has the coordnate of physcal check-n poston and tagged frends who are stayng together wth the check-n user. Accordng to the address of the LBS, we can calculate the dstance between the check-n poston and LBS servce provder and use t to fnd the canddate customers. For LBS j, a dstance score of certan check-n nformaton was used to express the dstance degree from the poston of check-n to LBS j. We denote CI as the number of total tag-ns contaned n all check-n nformaton. The rank value of check-n nformaton CI toward LBS j s represented as rank(ci, LBS j ). If the rank value of check-n nformaton CI s the nearest one from LBS j,, then rank value of check-n CI s equal to 1. The dstance score of check-n nformaton CI toward LBS j s normalzed as formula (4). CI rank( CI, LBS j ) actvty _ rank(, LBS j), (4) CI For LBS j, the actvty score of customer s measured by averagng all the dstance score of each check-n nformaton whch customer had publshed or was tagged n. The actvty score for customer u wth respect to LBS j s measured by aggregatng all the check-n ranks of user who was tagged and s formulated as: Actvty( u, LBS j ) c rank( u, LBS ) Tag( ), (5) Tag( ) j where c and u s the set of check-n n whch user u was tagged. Fnally, the fnal GF(u,LBS j ) Tag s obtaned by aggregatng Statc and Actvty scores and expressed as: GF(, LBS j ) Statc(, LBS j ) Actvty(, LBS j ), (6) 3.2 Endorser Canddate Network Constructng Mechansm After dscoverng the target users, an endorser canddate network can be constructed wthn the vald servce scope of LBS. Endorser canddate E refers to people or organzaton wth suffcent capablty to serve as advertsng seeds for these target users and are predefned, regstered or contracted, wth ftness the LBS servce type and wthn the vald servce scope of LBS, as shown n Fgure 1 step Seed Endorser Identfyng Mechansm Havng constructed the endorser canddate network, the system wll dentfy the stable seed endorsers from the network and delegate them to delver the ad. In ths paper, users socal factors are analyzed to fnd out the stable seed endorsers. The followng modules are desgned to dentfy stable seed endorsers Socal Influence Analyss Module Excellent endorsers should have a great nfluence on ther customers. The greater the nfluence, the more people he or she can nfluence. In ths sense, the nfluence of all canddate users should be analysed. Popularty nfluence s used to measure the popularty degree of a user s posts. The

9 messages posted by a user mght be responded to, forwarded, or even just marked as lke. These three knds of feedback actons from frends are collected to calculate the popularty degree of a user. We denote post u ) as the set of messages posted by user, whle resp u ), ford u ), and ( mark u ) represent the sets of s posted messages that are responded to, forwarded, and marked by ( other users respectvely. The formula for the socal nfluence of user ( s represented below: resp( ) ford ( ) mark( ) So_ F( ), (7) post( u ) Moblty Influence Analyss Module In general, customers are wllng to use the servce whose locaton s close and convenent to reach. Transportaton convenence s one of the mportant factors affectng the customers purchase ntenton. If the transportaton convenence s greater, more people wll be attracted to use the LBS. In ths sense, the movng trajectores of all canddate users should be analysed. Frst, we consder the nfluence of dstance between the current poston of SPU and LBS. Generally, the longer the dstance between SPU and LBS servce provder, the lower the probablty that the SPU wll move to the LBS servce provder. On the contrary, the shorter the dsance between SPU and LBS servce provder, the lower the probablty of SPU movng to the LBS servce provder. Takng fgure 4 as an example, f u receves LBS ad at advertsng cell Endorser j, then the dstance from the Endorser j to LBS servce provder wll affect the u to use ths LBS servce. We denote d (Endorser j, LBS) as the dstance between the locaton at whch the target user receves the ad and the LBS servce provder. The formula for the dstance nfluence of user s represented below: Lo _ F( u ) d ( Endorser j, LBS ), (8) e u u u ( Fgure 4. Dstance nfluence for LBS advertsng. Next, we consder the locaton of the LBS servce provder s consstent wth u s movng drecton or not to decde whether the users wll go to use the LBS or not. Followng general behavour patterns, when u receves the LBS advertsng message, u wll go to use the LBS s decded by the drecton to the poston of LBS servce provder s consstent wth the movng drecton of u. If u s drecton of movement s consstent wth the poston of the LBS, then the probablty for u to use the LBS s hgh. On the contrary, f u moves n the opposte drecton wth LBS servce provder, then the probablty for u to use the LBS s low. We term ths factor as movng drecton effect (Md_F for short). The Md_F s measured as: ( Endorser, LBS j ) ( Path( ), Endorser ) Md _ F( u ) e, (9) When we select a stable endorser, the nfluence degree, the dstance, and the movng drecton to LBS servce are three mportant factors to be consdered. By ths reason, the three scores are

10 aggregated to get an overall score for the endorser. The endorser score (ES) for a typcal user be defned as below: ES u ) So_ F( u ) Lo _ F( u ) Mo _ F( u ), (10) ( We use the endorser score ES to rank all the users exstng n the endorser canddate network. The Top- K ranked users would be selected as endorsers to spread advertsng nformaton. 4 Performance Desgn In ths secton, we descrbe the experments used to verfy the proposed socal advertsng mechansm. The experments are conducted n a popular socal networkng webste, Facebook. The experment s desgned to send messages n prvate. On Facebook, when the message publsher delvers a message to a recever by the message sendng functon, only the recever can receve ths message. The experment s LBS ad wll be delvered n ths way to measure the source of LBS message. The LBS message used n the experment s a hyperlnk wth socal text (endorser + dstance to LBS + movng drecton) (See Fgure 5). When the partcpants clck the hyperlnk, they wll be redrected to vst the webpage of product wth detaled nformaton. The category tree of LBS s blt accordng to the product category of Amazon. The nformaton collected from the Amazon s used to bld a category tree wth three general categores (Entertanment & Lvng, Consumer Product, and Computer, Communcaton & Consumer Electroncs). u can Fgure 5. Socal dffuson of LBS Ad The category tree s mportant and helpful n categorzng for users preference and the LBS type. Regardng the experment ads, all the reqred product nformaton for the experment s collected from or referred to the famous web stes lke Amazon, best Buy, and Yahoo. Ths provdes varous knds of ads for the experment n ths research. After ad recevers vstng the web ste, they can clck the hyperlnk under the LBS ad to provde ther feedbacks on the ads receved. From the feedbacks of the recevers, we collect the followng nformaton (the user d of the recever, the user d of sender, the degree of preference matchng, the approprateness of the ads sender, and the wllngness of usng LBS nspred by the sender). The nformaton collected from the frst two questons would be used to understand the dffusng path and touchng the target users or not. The rest questons would be used to collect the nformaton of recevers subjectve comments as the ndcators for evaluatng the effectveness between dfferent dffusng strateges. In the experment, the snowball samplng method (Wlson and Ncholas, 2008) s used to construct the network structure of the experment. Intally, we nvted 7 users, who are wllng to authorze us to collect ther socal nformaton. Wth ther help, we further nvte ther frends and frends-of-frends. After removng these users wthout any check-n of whch poston falls n LBS s

11 vald servce scope durng recently three months. Fnally, there are totally 67 users partcpated n the experment (male: 56% and female: 44%). In order to decrease the lmtaton made by the lackng profle nformaton, the partcpants are requested to select at most fve types of nterested nformaton when they frst jon the experment. These nformaton would be taken as the statc preferences of users. The dynamc preferences are dscovered by the CKIP, a system for Chnese word segmentatng. To dscover the dynamc preference of user SPU, the posts wthn recent three months would be collected and then processed by CKIP to token the frequent appearred word. If ths word represents a new nterest of user SPU, t would be added nto the preferences of user SPU. To explot the effcency of the proposed moble socal advertsng approach, we compare the performance of our approach wth others approach. In ths paper, three dfferent endorser selectng strateges are desgned to dentfy the set of seed endorsers. The three dfferent strateges are as follows. (1) Moble Socal Endorser approach (MSE): ths advertsng approach s based on the proposed archtecture. (2) Hot Spot approach (HS): ths approach dentfes the seed endorsers by drectly evaluatng the canddate endorser stes whch has the most target users move across. (3) Random selecton approach (Random): In ths approach, the seed endorsers are selected randomly. These three dfferent advertsng strateges wll be desgned as a set n each experment to collect the reacton of recevers. Each strategy prepares smlar but not the same ads for the target recevers. So n each experment, we wll get feedbacks for three dfferent approaches. 5 Experment Results In ths sesson, we dscuss the results of the experment and nsghts dscovered. The attracted rate of the target users and ad effectveness level are used to measure the performance of varous advertsng strateges. 5.1 Target User Attracted Rate Target users are the users whose preference matches the ad to dstrbute. The target user attracted rate (TAR) s defned as the rato of the target users who actually drve to the destnaton of LBS servce provder and can be calculated as: T A TAR, (11) T where s the set of users who drve to the destnaton of LBS servce provder. The target user A attracted rates of dfferent advertsng approaches are shown n Fgure 6: Fgure 6. Average TARs of dfferent advertsng strateges

12 In average, the target user attracted rate TAR of MSE s 41.17% and the TAR of the HS s 35.35%. The average TAR of the random approach s 15.78%. The results demonstrated that MSE performs better than other advertsng approaches n attractng the targeted users to destnaton of LBS servce provder. 5.2 Ad Effectveness In the followng, we examne the feedbacks and ratngs collected from the users dffused to verfy the effectveness of the advertsng strateges. Three ndcators, satsfacton on ad nformaton, sender approprateness, and purchase wllngness, are used to measure the effectveness of advertsng. The dstrbutons of these three ndcators among three dfferent strateges are depcted as below: Fgure 7. The average score on three dfferent strateges From Fgure 7, the MSE approach has hgher average ratng scores than other two strategy approaches. The hgher average score of MSE n the preference matchng shows that MSE can effectvely send the ftter LBS ads to target users. The hgher average score of MSE n the sender approprateness suggests that MSE can effectvely select out stable spreaders whch are favoured by the recever to share the ads. Also, when dscussng to the wllng accepts LBS, MSE gans a better persuasve effect than HS and Random selecton approaches. 6 Concluson In ths paper, we study the problem for attractng the smart phone users to the destnaton of LBS servce provder. To solve ths problem, we proposed a socal endorser advertsng framework, whch consder the socal nfluence of endorsers and the dstance and the movng drecton of targeted users to the LBS servce provder. To mprove postve mpressons through a successful socal advertsng we frst fnd out the rght target audences to receve an LBS ad and explot the socal nfluence and trajectory analyss approach to dentfy the rght locaton of endorsers to dssemnate the LBS ad to do so. The experment results demonstrated that proposed mechansm performs better than other advertsng approaches n attractng the targeted users to the destnaton of LBS servce provder. There are several lmtatons and research ssues that can be studed further. Frst, constraned by the users concerns about prvacy, the experment was only conducted n a small part of the whole networks. Ths lmts us to use a more rch and comprehensve data to vald the proposed goal. Wth the comprehensve data of the whole users, t mght conduct more sgnfcant experment results. Second, locaton-based group-recommendaton s also a new area for advertsng. Wth the help of GPS and socal meda, we fnd a set of frends nearby and recommend what they lke to consume together. Lastly, the trend of synchronzng nformaton to operate several socal meda should be further examned. It wll be a new ssue to measure and manage the socal actvty among dfferent socal meda platform. Ths ssue also affects the dea of the nature of nformaton delverng and advertsng.

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