An Implicit Rating based Product Recommendation System

Transcription

1 An Implct Ratng based Product Recommendaton System Cheh-Yuan Tsa Department of Industral Engneerng and Management Yuan Ze Unversty, Taoyuan Cty, Tawan Tel: (+886) ext. 2512, Emal: Sh-We Shen Department of Industral Engneerng and Management Yuan Ze Unversty, Taoyuan Cty, Tawan Abstract. Collaboratve flterng (CF) s one of the most popular approaches n recommendaton systems. Typcally, collaboratve flterng looks for users who share the same ratng patterns wth the actve user and uses the ratngs to calculate a predcton for the actve user. However, explct ratngs are not always avalable n many physcal stores. To solve the problem, ths paper develops a recommendaton system that can derve user preference ratngs from users purchase hstory wthout any explct feedback provded by user. In addton, ths research also consders the tme nterval between purchased tme and current tme (recency) and tem relatonshp. Fnally, max-mn fuzzy theory s used to combne the two factors. Through experments, the proposed approach shows much better performance than the system whch consders only the factor of tem purchase frequency. Ths study also shows that the performance of the proposed system outperforms the one wthout consderng the tme nterval between purchased tme and current tme as well as tem relatonshp. Keywords: recommendaton systems, mplct feedback, collaboratve flterng, max-mn fuzzy. 1. INTRODUCTION In the nformaton overloaded age, users usually hope to fnd what they want wthout wastng much tme. To solve ths burden, recommendaton systems have been emerged n response to ths problem. Fundamental knowledge and technques for developng recommendaton systems have been proposed n recent decades, ncludng content-based flterng (Pazzan and Bllsus 1997), collaboratve flterng (Yu et al. 2004, Konstan et al. 1997) and hybrd approaches (Balabanovc and Shoham 1998, Salter and Antonopoulos 2006, We et al. 2008). Collaboratve flterng reles on users whose preferences (or nterestng) are smlar to those of target users and recommends tems that users have lked. Because collaboratve flterng depends on user behavor, t attracted much attentons resultng n sgnfcant progress and beng adopted by some successful commercal systems, ncludng Amazon (Lnden et al. 2003) and Netflx (Bennet and Lannng 2007). In many collaboratve flterng systems, user behavor can be derved from user s nteracton wth the system whch s called feedback. Feedback can be dvded nto explct feedback and mplct feedback. Explct feedback refers to the relevant nformaton provded by users drectly. The typcal explct feedback s the user ratng on tems (Roy et al. 2010). However, explct feedback s not always avalable n practce. Thus, some recommendaton systems dscuss mplct feedback ssues. Research n mplct feedback can be dvded nto two parts. One focuses on usng demographc data such as age, educaton, ncome or gender to fnd a set of users smlar to the target user and calculate the ratng by the set of smlar users (Zou et al. 2009, Lu and Shh 2005, Lu et al. 2009, Wang and Zhou 2012). For example, Zou et al. (2009) used demographc data to fnd smlar users and combned other nformaton such as how many tmes users vsted museum and how long they stayed n museum to recommender. Wang and Zhou (2012) utlzed demographc data to defne ratng of dfferent attrbutes and calculate the ratng by what attrbutes of a target user has. Although these researches use demographc data to fnd smlar group of users, they do not take user s real transacton data nto consderaton and stll requre some explct feedback. Another group of researches n mplct feedback focuses on how transform user preference ratng from purchase hstory, browsng hstory, search patterns, or even mouse movements (Lee et al. 2010, Km et al. 2005, Cho et al., 2012). For example, Lee et al. (2010) proposed a pseudo ratng matrx whch defnte ratng by tems lunch tme and users purchase tme. Cho et al. (2012) proposed an equaton to transform user ratng by how many tmes

2 user buys an tem. However, these researches don t consder the dfference of tme nterval between purchased tme and current tme. To solve the above problems, the purpose of ths paper s to develop a recommendaton system to derve user preference ratngs from users purchase hstory wthout usng explct feedback provded by the user. Ths recommendaton system takes purchase frequency, purchase cycle tme and purchase tme-nterval to establshed user preference ratng. Moreover, ths research also consders the tme nterval between purchased tme and current tme and tem relatonshp. Fnally, ths research uses max-mn fuzzy theory to combne these factors. 2. RESEARCH METHOD The framework of the proposed mplct ratng based product recommendaton system conssts of three man steps as shown n Fgure 1. They are (1) constructng user preference ratng usng purchase frequency, cycle tme and purchase tme-nterval; (2) predctng user preference ratng usng best-n-neghbors; (3) generatng an tem recommendaton lst through neghborhood of the target user wth consderatons of purchase recency and tem assocaton. Establshng user preference ratng usng transacton data User preference ratng database preference ratng Predcton usng Best-N-Neghbors Target User Transacton database Purchase Recency consderaton Usng fuzzy theory to ntegrate user preference ratng, purchase recency and tem assocaton Recommendaton lst tem assocaton consderaton Fgure 1: The framework of the proposed mplct ratng recommendaton system. 2.1 Establshng User Preference Ratng usng Transacton Data As mentoned before, t s qute often that user preference ratng s dffcult to obtan through explct feedback. Therefore, ths research constructs user preference ratng usng transacton data. The preference ratng constructon s based on three observatons. Frst, f the purchase frequency (count) for an tem s hgh, the preference ratng for the tem wll be hgh. Second, f the cycle tme between two purchases for the same tem s short, the preference ratng for the tem wll be hgh. Thrd, f the tme-nterval between transacton tme and current tme s short, the preference ratng for the tem should be hgh. Let T u,,t represent tth transacton tme that user u purchases tem. CT u,,t s the cycle tme that that user u buy tem between tth and t+1th transactons. It can be evaluated as: CT T T (1) u,, t u,, t1 u,, t Let TT u,,t represent tme-nterval between t+1th transacton tme and current tme T now for user u and tem. TT u,,t can be evaluated as: TT T T (2) u,, t now u,, t 1 To construct user preference ratng of user u for tem, PD u,, the followng equaton s formulated: PD t MnCT 1 ( ) CT TT u,, t u,, t MnCT s the mnmum cycle tme for all users who ever purchased tem, whch can be derved by: all u, all t u,, t (3) MnCT mn { CT } (4) 2.2 Preference Ratng Predcton usng Best-N- Neghbors Typcally, a collaboratve flterng recommendaton system predcts the ratng of an tem for a target customer based on how smlar customers rated the same tem prevously. In ths research, we use Equaton (7) to calculate the smlartes between target user u and other user v: ( PDu, Pu, v) ( PDv, Pv, u ) (7) Muv, ; SIM 2 2 ( PD Pu, v) ( PD Pv, u ) M u,, u, v; M v u, v; P M (8) here, uv P s the average user preference ratng for user u n whch tems exst n both user u and user v, and M u,v s the M PD

3 set of tems whch exst n transactons made by user u and user v. After obtanng SIM u,v for all user v, a set of neghbors smlar to the target user u wll be retreved. In ths research, the best-k-neghbor technque s adopted. That s, top-k users who have hgher smlarty wth the target user are consdered as neghbors.the predcted preference ratng for tem by the target user u, R u, can be estmated by the follow equaton: R vn PD vn SIM v, u, v SIM 2.3 Recency Consderaton It s clear that user s desre for an tem wll reduce f the tme between current tme and last purchase tme s long. Smlarly, f the tme between current tme and last purchase tme for an tem s too short, the purchase desre for the tem wll reduce also. Therefore, the predcted preference ratng for an tem should be adjusted accordng to the above dea. Let WT u, be the adjusted weght for user u and tem and can be defned as: RT WT f RT CT CT (9) 0.5 u, ( ) u, (10) CT WT f RT CT 0.5 u, ( ) u, (11) RTu, where RT u, s tme between current tme and the last tme of purchasng tem for user u, also called recency for tem and user u. CT s the average cycle tme of buyng tem for user u. If user u dd not buy tem more than one tme, we wll take CT v, of user u s neghbor v as CT. 2.4 Item Assocaton Some tems are often purchased together. Therefore, tem assocaton should be nvolved when we generate tem suggeston lst. Let Supp({, }) represent how many tmes tem and tem happen n same transacton data. Int({, }) represents two tems ntmacy whch can be defned as: Supp({, '}) Int({, '}) Supp({ }) Supp({ '}) Supp({, '}) (12) 2.5 Fuzzy Assocaton for Informaton Integraton Ths research uses fuzzy theory to ntegrate two tems ntmacy, user preference, and the tme between current tme and last purchase tme (recency). Max-mn fuzzy theory s based on the rato of the number of all maxmum far domnatng sub vectors to the number of all possble sub vectors. It s shown how ths defnton extends the maxmum farness relaton, how t helps to solve the problems wth maxmum farness, and how t numercally emphasses farness. Max-mn fuzzy theory farness can be a formally effcent defnton of a farness concept. Let F u, represent the strength preference that user u wll buy tem, whch can be derved by F R WT (13) u, u, u, where R u, s the predcted preference ratng for tem by the target user u and WT u, s the adjusted weght for user u and tem. Next, mn-max normalzaton s appled to F u, so that modfed strength F u, wll be between 0 and 1. Thrd, ths research uses the max-mn composton proposed by Zadeh (1965) to generate an recommendaton lst for the target user. Let be the vector of modfed strength F u,, whch s defned as: au a [ F ', F '... F ' ] (14) u u, A u, B u, Let the set of the two tem ntmacy n Equaton (12) be a fuzzy relaton, IR. That s, IR x, y,int {x, y} ( x, y) (15) Specfcally, the proposed fuzzy assocaton FA[ au, IR )] can be defned as: new au new FA[ au, IR] au (16) where s the resultng vector after conductng fuzzy assocaton. That s, the new vector a new can be obtaned by: where new u u, A u, B u, a [ FTI, FTI.., FTI ] (17) FTI [ F ' Int({ x, y})] max mn[ F ', Int({ x, y})] u, u, u, all, and respectvely. represent the fuzzy max and fuzzy mn 3. EXPERIMENTAL ILLUSTRATION To test the feasblty of the proposed mplct ratng based product recommendaton system, a smple shoppng store s used as an example. It s assumed that there are sx user types n whch each user type has unque shoppng patterns. Table 1 summarzes shoppng patterns of each user type. For example, customers wth type I vsts the shoppng store wthn fve to nne days and purchases no greater than eght tems n each vst.

4 Table 1: The shoppng patterns for sx user types. User Type Interval arrval tme (day) Maxmum number of purchases I [5, 9] 8 II [6, 10] 8 III [9, 13] 13 IV [10, 14] 13 V [14, 18] 18 VI [15, 19] 18 In ths shoppng store, there are 120 product tems avalable where the average purchase cycle tme for each tem s classfed as short term, md-term or long-term. The average purchase cycle tme for short-term tems such as lke cookes, frut and fresh food s around two weeks. The average purchase cycle tme for md-term tems such as mcrowave food and nstant noodles s around one month. The average purchase cycle tme for long-term tems such as lke shampoo and tolet paper s over one month. In addton, dfferent user types have dfferent shoppng patterns. For example, users of type V tends to buy baby dapers n whch users of other types seldom buy ths. Moreover, the average purchase cycle tme for tems for dfferent user types mght be dfferent. 3.1 Case Illustraton The proposed mplct ratng based product recommendaton system s mplemented usng C# and tested on a PC wth Core GHz and 8GB memory. In ths llustraton, the total user number s 300; the number of neghbors s 25, and the number of recommended tems s 5. The recommendaton system wll re-evaluate every smulaton day. For example, f the smulaton day s day 741, the recommendaton system wll consder day 365 to day 740 as transacton data. It means the recommendaton system wll update preference matrx every day and provde users updated recommendaton lst. In order to establsh user preference, the system wll read transacton data frst and then establsh user preference. Ths research constructs user preference ratng usng transacton data. Through Equaton (3), user preferences can be derved. Next, the smlartes between user u and user v can be calculated usng Equaton (7). Notes that the equaton only consders the average user preference ratng of whch tems both exst n user u and user v. Accordng to Equaton (9), the predcted preference ratng for each tem can be calculated. To understand the performance of the proposed system, four dfferent Models are compared. Cho et.al (2012) proposed a recommended method computed solely based on the purchase data of user u. In Cho et al. (2012), the preference ratng for user u and tem, called Model 0, s defned as how many tmes user u have purchased tem dvded by how many tmes tem have be purchase by all users. The recommended result usng the predcted preference ratng generated by Equaton (9) s called Model 1. The recommended result usng the strength preference ratng generated by Equaton (13) s called Model 2. The recommended result usng the ntegrated preference ratng generated by Equaton (16) s called Model 3. For example, f user U74 vsts shoppng store at day 731. The transacton record shows that U74 purchased 7 tems of I7, I13, I19, I25, I37, I118 and I117 at that day. If the number of recommended tems s set as 10, for the U74 the number of correct recommended tems for Model 0 s 5, Model 1 s 6, Model 2 s 6 and Model 3 s 7. Smlarly, f U241 vsts shoppng store at day 731 and he/she purchased 12 tems as I1, I2, I11, I17, I23, I29, I35, I41, I53, I59, I96, I98 and I116. The number of correct recommended tems for U241 s 6 for Model 0, 8 for Model 1, 9 for Model 2, and 9 for Model 3. Table 2 shows the dfferent model s recommended lsts for U74 and U241 at day 731. Table 2: A set of recommended lsts for user U74 and U241. Day User 731 U U241 Model type Model 0 Model 1 Model 2 Model 3 Model 0 Model 1 Model 2 Model 3 Recommended tems I111, I113, I117, I118, I99, I115, I37, I19, I31,I112 I13, I111, I25, I117, I118, I37, I49, I97, I19, I31 I13, I19, I25, I97, I117, I118, I31, I37, I49, I113 I7, I13, I19, I25, I117, I118, I31, I37, I49, I97 I1, I11, I47, I33, I29, I35, I114, I96, I98, I93 I1, I11, I53, I17, I48, I33, I29, I2, I35, I41 I1, I2, I11, I17, I23, I29, I35, I41, I48, I53 I1, I2, I11, I23, I29, I35, I41, I53, I17, I Performance Comparson No. of correct tems In order to understand the performance of the proposed method, recall, precson, and F-score s used to evaluate the qualty of recommendatons. Recall s the fracton of the correct recommended tems dvded by the total number of recommended tems by a recommendaton method. Precson s the fracton of correct recommended tems dvded by the total number of tems that a user purchased. F-Score combnes precson and recall to evaluate the qualty of a recommender

5 In ths experment, the number of customers s 300 and number of neghbors s 25, and the number of recommended tems s 5. Snce the smulaton program contans random procedure, fve dfferent datasets are generated and tested for dfferent Models. Table 3 shows the varance among the fve datasets s relatvely small n all models. Through Table 3, we can fnd the fve data F- score varance by all models are all small than Because the varance are so small, that we assume there are no sgnfcant dfferences between data 1 to data 5. In the followng dscusson, all experments wll be conducted fve tme and take the average of the fve experments as the fnal value. Table 3: The average of F-scores under dfferent number of users. Model Type Sum Mean Varance Model Model Model Model CONCLUSIONS In the nformaton overloaded age, users usually want to fnd what they want wthout wastng much tme. To solve ths burden, recommendaton systems have emerged n response to ths problem. Among them, collaboratve flterng approach s one of the most popular ones. However, many collaboratve flterng recommendaton systems use explct feedback (ratngs) whch are collected drectly from users to nfer the possble recommendaton. However, n many cases, explct feedback s hard to obtan and not always avalable n practce. To solve the above problems, ths paper develops a recommendaton system to derve user preference ratngs from users purchase hstory wthout any explct feedback provded by the user. Ths recommendaton system takes purchase frequency, purchase cycle tme and purchase tme-nterval to establshed user preference ratng. In addton, ths study uses max-mn fuzzy theory to combne tme weght and tem assocaton to get more accurate concluson. Some possble extensons are summarzed as follows. Ths research s based on smulaton data. Therefore, testng the proposed system n a practcal shoppng store should be helpful. In practcal stuaton, shoppng store mght gve dfferent dscount to dfferent tems. The sale prce mght be changed and become a sgnfcant factor affectng user purchase behavor. In the future, sale prce can be taken nto consderaton when makng product suggestons.the proposed recommendaton system s desgned for beneftng customers. It mght be nterestng f further research can take company proft n to consderaton when makng suggeston. Currently, the specal events and seasonal attrbutes are not taken nto account. For example, ce cream s usually purchased n summer, ngredents for hot pot are usually sold n wnter, and candy or toys wll popular before specal holday. The future work can consder the nfluences of tme perods. REFERENCES Balabanovc, M. and Shoham, Y. (1998) Content-based, collaboratve recommendaton. Communcatons of the ACM, 40(3), Bennett, J. and Lannng, S. (2007) The Netflx Prze, Proceedngs of KDD cup and workshop. Cho, K., Yoo, D., Km, G., and Suh, Y. (2012) A hybrd onlne-product recommendaton system: Combnng mplct ratng-based collaboratve flterng and sequental pattern analyss. Electronc Commerce Research and Applcatons, 11(4), Konstan, J., Mller, B., Maltz, D., Herlocker, J., Gordon, L., and Redl, J. (1997) GroupLens: Applyng collaboratve flterng to usenet news. Communcatons of the ACM, 40(3), Km, Y. S., Yum, B. J., Song, J., and Km, S. M. (2005) Development of a recommender system based on navgatonal and behavoral patterns of customers n e- commerce stes. Expert Systems wth Applcatons, 28, Lnden, G., Smth, B., and York, J. (2003) Amazon.com recommendatons: tem-to-tem collaboratve flterng. IEEE Internet Computng, 7, Lu, D. R. and Shh, Y. Y. (2005) Hybrd approaches to product recommendaton based on customer lfe tme value and purchase preferences. The Journal of Systems and Software, 77, Lu, D. R., La, C. H., and Lee, W. J., (2009) A hybrd of sequental rules and collaboratve flterng for product recommendaton. Informaton Scences, 179(20), Lee, S. K., Cho, Y. H., and Km, S. H., (2010) Collaboratve flterng wth ordnal scale-based mplct ratngs for moble musc recommendatons. Informaton Scences, 180(11), Pazzan, M. and Bllsus, D. (1997) Learnng and revsng user profle: the dentfcaton of nterestng web stes. Machne Learnng, 27(3), Roy, S. B., Shem, A. Y., Chawla, A., Das, G., and Yu, C. (2010) Space effcency n group recommendaton. The VLDB Journal, 19(6),

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