An Item-Targeted User Similarity Method for Data Service Recommendation

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1 01 IEEE 16th Internatonal Enterprse Dstrbuted Object Computng Conference Workshops An Item-Targeted User Smlarty Method for Data Serce Recommendaton Cheng Zhang, Xaofang Zhao Insttute of Computng Technology Chnese Academy of Scences Bejng, Chna Janwu Wang San Dego Supercomputer Center Unersty of Calforna, San Dego San Dego, U.S.A Abstract Memory-based methods for recommendng data serces predct the ratngs of acte users based on the nformaton of other smlar users or tems, where the smlarty algorthm always plays a key role. In many scenaros, we fnd that the smlarty of two users always show dfferent effecteness when predctng dfferent ratngs. Normal smlarty algorthms usually do not count the dfference, snce they orgnate from statstc and algebra felds and do not drectly am at recommendatons. Ths paper proposes a noel method to amend the user smlarty generated by a normal smlarty algorthm to more accurately descrbe the effecteness of the smlarty on a targeted tem. We apply our method to mproe the Pearson Correlaton Coeffcent (PCC) algorthm whch s one of the most commonly used smlarty algorthms. The experment results on some practcal datasets show that our method s slghtly better than the orgnal PCC algorthm for predctng ratngs n recommendatons. Keywords-data serces; recommender system; user smlarty; collaborate flterng I. INTRODUCTION Data serce recommendaton predcts the nterest of data serces(tems) for an acte user by collectng ratng nformaton from other smlar users or tems, and related technques hae been wdely employed n some famous web communtes [14], such as Amazon 1 and Ebay. The key dea s that the acte user wll prefer those tems that are preferred by smlar users. Memory-based approaches hae been deeply studed and wdely used n practce, whch can be dded n two categores: user-based [4, 8, 10] and tem-based [17, 6]. When estmatng an unknown test ratng, memory-based approaches wll frstly collect the smlar users or tems by some smlarty algorthms, such as Pearson Correlaton Coeffcent (PCC) [16] and Vector Space Smlarty (VSS) [4]. Then, the ratng nformaton of smlar users or tems wll be handled by some predcton algorthms to produce the estmated alue for the test ratng. In many scenaros, we fnd that the smlarty of two users always show dfferent effecteness when predctng dfferent ratngs. For example, user u and are smlar users snce they share the preference on tems 1,, 3, 4, whch are all data serces on comedy moes. Item 5 and 6 are unknown ratngs of user where 5 s a data serce on comedy moes and 6 s a data serce on scence-fcton moe. The ratngs of user u on tem 5 and 6 wll be used to predct the ratng of 5 and 6 for user respectely accordng to the smlarty between u and. The smlarty wll be more effecte to predct the ratng of user on tem 5 than that on tem 6, due to our common experences that t s unreasonable to nfer a user s preference on a scencefcton moe from hs/her preference on comedy moes. Smlarly, f user w and are smlar because they both lke seeral acton moes, the smlarty between user u and wll be more effecte than the smlarty between w and to predct the ratng of user on tem 5. Yet normal smlarty algorthms usually do not count such dfferences, snce they generate from more unersal statstc prncples and do not drectly am at recommendatons. So, there s a latent error factor when applyng such algorthms nto the recommendaton tasks drectly. Based on the aboe obseratons, we argue that the aspect of the predcted tem should be elaborately counted n the user smlarty calculaton to get better recommendatons. Ths paper proposes a noel method to amend the user smlarty generated by a normal smlarty algorthm to descrbe the effecteness of the smlarty on a targeted tem more accurately. Gen a specfc smlarty algorthm, our method can work n two ways: one s called Correcton Polcy, whch uses tem relatonshps to adjust the user smlarty; the other s named Precson Polcy, whch selects the more sutable co-rated tems to calculate user smlartes. We note that ths paper focuses on the effecteness descrpton of the user smlarty on dfferent tems and the nfluence analyses of the effecteness on the ratng predcton, rather than addressng a combned model to get the optmal recommendaton as we hae learnt from the Netflx Prze competton [3]. So, we apply our method to mproe the Pearson Correlaton Coeffcent (PCC) algorthm whch s one of the most commonly used smlarty algorthms. The experment results on some practcal datasets show that our method outperforms the orgnal PCC algorthm for predctng ratngs n recommendatons. The rest of the paper s organzed as follows. The next secton prodes a bref reew of related work. Secton 3 presents our method of the tem-targeted user smlarty. The /1 $ IEEE DOI /EDOCW

2 results of an emprcal analyss are presented n Secton 4, followed by a concluson n Secton 5. II. RELATED WORK A. Defntons and Notatons Let I { 1,,..., n } be a set of tems, U {u 1, u,..., u m } be a set of users, and I u be the set of tems rated by u. The relatonshp between users and tems s denoted by an MN matrx, where each entry r represents the ratng of tem made by user u. Let r u represent the aerage rate of user u on all rated tems. B. Recommendaton Methods We wll frst brefly reew the deelopment n recommendaton. Generally, exsted algorthms fall n ether of the followng two categores: memory-based flterng and model-based flterng [7]. 1)Memory-based Approaches.Memory-based approaches essentally are heurstcs to predct a mssng ratng by aggregatng the ratng of k-neghbors who hae preously rated the tem. To dentfy the k-neghbors, seeral algorthms hae been proposed to compute the smlarty between each par of users or tems. The common-used aggregaton s to compute the weghted aerage of the k- neghbors ratngs on the partcular tem [1]. Consderng the fact that dfferent users may use the ratng scale dfferently, the weghted sum uses ther deatons from the aerage ratng of the correspondng neghbor. Two types of memorybased approaches hae been studed: user-based [4, 8,10] and tem-based [17, 6]. User-based approaches predct the ratngs of acte users based on the ratngs of smlar users found, and tem-based approaches predct the ratngs based on the nformaton of the computed smlar tems. )Model-based Approaches. In contrast to memorybased methods, model-based approaches use the tranng examples to learn a predefned model, whch s then used to make ratng predctons. There are arous approaches n ths category. Typcal examples nclude clusterng model [1, 0, 11], aspect models [9, 19] and latent factor models [5]. Dmensonalty reducton and matrx reconstructon technques are often the subject of dscusson [, 18]. Model-based approaches are often tme-consumng to buld and update, and cannot coer as derse a user range as the memory-based approaches do [3]. In order to take the adantages of memory-based approaches and model-based approaches, hybrd approaches hae been studed recently. For example, Bell et al. combned the output of multple recommender algorthms to mproe performance [1, ]. 3)Smlarty Computatons. As mentoned aboe, smlarty algorthms are used to collect the smlar neghbors n memory-based approaches, and drectly affect predctons accuracy. Varous approaches hae been used to compute the smlarty between users or tems n recommender systems. We wll take user smlartes as examples to explan the prncpal of these approaches. The two most popular approaches are Pearson Correlaton Coeffcent (PCC) [16] and Vector Space Smlarty (VSS) [4]. PCC algorthm [16] measures the smlarty between two users based on the tems that both users hae rated n common: Sm( ) I I r ) u r ) u I r ) r ) where Sm( ) denotes the smlarty between user u and user and belongs to the subset of tems co-rated by both users u and. Obously, Sm( ) s rangng from [0, 1]. VSS algorthm [4] measures the smlarty of user u and user by computng the cosne angles between the ratng ectors of them: I u I ru, I I (1) ru, r, () Sm( ) cos( ) r where u and denotes the ratng ectors of user u and respectely. Generally, PCC approach s more accurate than VSS approach, snce t consders the factor of the dfferences of user ratng styles [14]. The adjusted cosne smlarty compensates ths drawback by subtractng the correspondng user aerage from each co-rated par and has been shown more effecte for measurng tem smlartes [17]. On the other hand, PCC algorthm oerestmates the smlartes of users who happen to hae rated a few tems dentcally, but may not hae smlar oerall preferences [15]. Herlocker etc. proposed to use the followng equaton to modfy the user smlarty [15]. Max( Iu I Sm' ( ) γ, γ ) Sm( ) Here s a threshold parameter. Ths method soled the problem when only few tems rated but n case that when I a I b s much hgher than, the modfed smlarty wll be larger than 1. In order to bound the smlarty alue to the nteral [0,1], Hao etc. proposed another equaton [14]: Mn( Iu I Sm' ( ) γ (3), γ ) Sm( ) (4) Some other researchers address the fact that the smlarty algorthms would not work well when there are few tems co-rated by both user u and. It was emprcally shown that the ratng predcton accuracy could mproe f we frstly smooth the mssng data of the user-tem matrx [14, 1]. III. ITEM-TARGETED USER SIMILARITY The basc dea of the tem-targeted user smlarty s to amend the user smlarty generated by a normal smlarty algorthm to descrbe the effecteness of the smlarty on a targeted tem more accurately. We wll explan our method 173

3 based on the PCC algorthm snce t s one of the most commonly used smlarty algorthms. Our method can work n two ways to calculate the tem-targeted user smlarty. In the followng two sub-sectons, we wll dscuss them n detals. A. Correcton Polcy The key dea of Correcton Polcy s to adjust the orgnal user smlarty usng a correcton factor whch measures the smlarty effecteness on the predcted ratng, and the orgnal user smlarty s calculated by a normal smlarty algorthm, such as PCC. The correcton factor s as follows: SmQau Sm( j, j IU I ( (5) Iu I where s the predcted ratng. Gen a specfc tem, the aboe equaton ealuates the qualty of the user smlarty accordng to the smlarty relatonshps between the tem and the tems whch are shared by the user u and. We call ths factor as Smlarty Qualty. By defnton, the alue of Smlarty Qualty les n the range [0,1]. Also, the more smlar the shared tems and the tem are, the larger the SmQau alue s. Based on Smlarty Qualty, the resed smlarty between two users for a specfc tem s as follows: Sm( Sm( ) SmQau( (6) Here Sm( ) les n the nteral [-1, 1]. Sm( wll be an order of magntude lower than Sm( ) snce the alue of SmQau( les n the nteral [0, 1]. So the contrbuton of smlar users on the predcted ratng wll be largely reduced. It s a reasonable nference f we predct the unknown ratng usng the resed smlarty, the predcted ratng wll be less than t should be. Therefore, a recoery functon s requred to ensure the resed smlarty can keep the effecteness of the smlarty qualty whle remong the harmful mpact mentoned aboe. So that the smlarty s further resed as: Sm( F( Sm( ) SmQau( ) x F ( x), x[-1,1] (7) x Obously, when the alue of x les n [-1, 1], the nteral of F(x) s stll n [-1,1] and meanwhle F(x) s larger than the absolute alue of x. In Secton 4, we wll test the effect of the method on some practcal datasets. B. Precson Polcy The key dea of Precson Polcy s to select such co-rated tems that are smlar to the specfc tem to calculate the smlarty between user u and. Based on the PCC algorthm, we measure the tem-targeted smlarty between two users by addng another factor : Sm( j I (, ) >, )(, ) I Sm j u j ru r j r u α (8) r ) r ) j Where s the tem smlarty threshold, and Sm(, j) s computed by the normal PCC algorthm. The selecton of the parameter s mportant snce a bg alue wll always cause the shortage of smlar users, and a relate small alue wll not get rd of the mpact of the dssmlar tems. In practce, two smlar users do not always share such tems that are hghly smlar to the predcted tem. In order to adequately utlze the nformaton of such users, the temtargeted smlarty s calculated as the follow steps: Step 1: To compute the smlarty of two users by the Eq.1; Step : If the smlarty s less than, then t s recalculate by the Eq.8 Where s a alue assgned beforehand. The adantage of ths method s that we can dscoer the smlarty between users as much as possble, whch wll prode more useful nformaton for the predcton task. Ths combned method wll be referred to as c-precson Polcy and the raw precson polcy expressed by the Eq.8 s called r-precson Polcy. In Secton 4, we wll tune the parameter based on our experments and present the emprcal result. C. Recommendaton Framework Wth the aboe tem-targeted smlarty algorthm, there are stll seeral basc problems for recommendatons. One s how to select smlar users, and another s how to estmate the unknown ratng usng the smlar users nformaton. The frst queston comes from the fact that the smlar users wth a lower smlarty alue wll decrease the accuracy of the predcton. Although the tem-targeted smlarty may hae an adantage oer the orgnal smlarty, we stll cannot neglect ths problem. In ths paper, we adopt the method proposed n the paper [14] to deal wth ths problem. Before makng a predcton, a set of k most smlar users S(u) toward user u are frst generated accordng to: u j { u Sm( u, > u u} S( u) θ, (9) a a where Sm(u a, s calculated usng Eq.7 or Eq.8 respectely correspondng to the Correcton Polcy and the Precson Polcy, and s the smlarty threshold. Accordng to Eq.9, the canddate user wll be selected as the smlar user f the smlarty between the canddate user and the acte user s larger than, whch tells us that the selecton of s mportant. We wll tune the parameter based on our experments and present the emprcal results n Secton 4. To sole the second queston, a sum of the aerage ratng made by the acte user u and a weghted aerage of the smlar users ratngs on the specfc tem s used to generate the predcton on the unknown ratng: a 174

4 S( u) r ru + (10) S ( u) r ) Sm( Sm( where r _ u denotes the aerage ratng made by user u and Sm(, whch s computed by Eq.7 or Eq.8, s the tem-targeted smlarty between user u and user. Ths weghted sum of deatons from the smlar users aerage ratng specally take nto account the fact that dfferent users may use ratng scale dfferently. IV. EXPERIMENTS We dd multple experments to ealuate the effecteness of our method. In partcular, we address the followng ssues: How do the recoery functon F and the threshold affect the accuracy of predctons n the correcton polcy? How do the thresholds and affect the accuracy of predctons n the precson polcy? For the two tem-targeted smlarty polces, namely Correcton Polcy and Precson Polcy (ncludng c- precson polcy and r-precson polcy), whch one more effecte? Is the tem-targeted user smlarty better for predctons compared wth the user smlarty whch our method amends? A. Datasets Two datasets from moe ratng are used n our experments: MoeLens 3 and Netflx 4. We wll report the smulaton results n detals. TheMoeLens dataset contans 100,000 ratngs (1-5 scales) rated by 943 users on 168 moes, and each user at least rated 0 moes. The densty of ths datasets s 8.77%. To test on dfferent numbers of tranng users, we extracted a subset of 500 users wth more than 40 ratngs. The frst 00 users are used for tranng and we altered the tranng sze to be 50, 100, 00 users. The last 300 users are used for test, and each ratng of these users was predcted usng an all-butone polcy [4]. The Netflx dataset contans oer 100 mllon ratngs (1-5 scales) rated by users on moes. We randomly extracted a subset of 500 users wth more than 80 ratngs on 3000 moes. The frst 00 users are used for tranng and we altered the tranng sze to be 100, 00 users. The last 300 users are used for test, and each ratng of these users was predcted usng an all-but-one polcy. B. Metrcs The major crteron for ealuatng ratng-orented recommendaton algorthms s the ratng predcton accuracy [13]. Seeral technques hae been proposed to achee t ncludng Mean Absolute Error (MAE), Mean Squared Error (MSE), Normalzed Mean Absolute Error (NMAE), and so on. In ths paper, the Mean Absolute Error metrcs s selected snce t works well for measurng how accurately the algorthm predcts the ratng of a randomly selected tem [10]. MAE s defned as: MAE r r u u N, ˆ, (11) where r denotes the ratng that user u gae to tem, and r ˆu, denotes the ratng that user u gae to tem whch s predcted by our approach, and N denotes the number of tested ratngs. C. Impact of Recoery Functon and Fgure 1. Impact of parameter F and on MoeLens dataset. Fgure. Impact of parameter F and on Netflx dataset. As shown n Eq.9, when predctng the ratngs under the correcton polcy, we ge a threshold to decde the selecton of smlar users whch has been collected. By assgnng a dfferent alue to, the performance of the predcton could be affected. The alue of s ared from 0 to 1. When settng as 0, the algorthm uses all the smlar users to complete the predcton. When s set as 1, only users that are equal to the acte user wll be used to predct the unknown ratng. We tune the alue to show the performance on predcton. Fg.1 and Fg. show the performance changes when the threshold ncreases from 0 to 1. We can see that the 175

5 correcton polcy wll achee the best performance around 0. on both the MoeLens dataset and the Netflx dataset. The change trends are smlar for dfferent numbers of users. D. Impact of Parameter When we adopt the r-precson polcy to mplement recommendatons, parameter may affect the accuracy of the tem-targeted smlarty between two users snce t s used to decde whch tems wll be selected for computng the smlarty alue. When settng as 0, all shared tems between two users wll be used to compute the smlarty, and so the tem-targeted smlarty algorthm s equal to the normal PCC algorthm. In ths experment, we ared the range of from 0 to 1 wth a step alue of 0.1. Fg.3 and Fg.4 show how the parameter affects the accurcy of predctons on the MoeLens dataset and the Netflx dataset respectely. enables the algorthm to aod the recalculaton for the users wth a hgh smlarty alue. Based on the aboe tranng results, we tested the parameter on the MoeLens and the Netflx datasets. For the MoeLens dataset, we assgned 0.4 to and selected 50, 100 and 00 tranng users. For the Netflx dataset, we assgned 0.8 to and selected 100 and 00 tranng users. In ths experment, we ared the alue of from 0 to 1 wth a step alue of 0.1. Fg.5 and Fg.6 tell us how the parameter affects the accuracy of predctons based on the all-but-one polcy Fgure 5. Impact of parameter on MoeLens dataset Fgure 3. Impact of parameter F and on Netflx dataset. Fgure 6. Impact of parameter on Netflx dataset Fgure 4. Impact of parameter F and on Netflx dataset. From Fg.3, we can see that the r-precson polcy achees the best performance around 0.4 on the MoeLens dateset. Fg.4 shows that the r-precson polcy achees the best performance around 0.8 on the Netflx dateset. Although the optmal alue of s dfferent on the two datasets, the change trends are smlar for dfferent numbers of users on a certan dataset. E. Impact of Parameter We expect that parameter can make predctons based on the c-precson polcy more accurate n practce snce t As showed n Fg.5, gen 50, 100 and 00 tranng users respectely, ths c-precson polcy wll achee the best performance around 0.1 on the MoeLens dataset. Fg.6 shows that ths polcy wll achee the best performance around 0 on the Netflx dataset when selectng 100 and 00 tranng users respectely. Also, the changng tendences are also smlar at dfferent user numbers. Ths result tells us that the c-precson polcy hae no obous effects on mprong the predcton accuracy. F. Comparsons We note that ths paper focuses on descrbng the effecteness of the user smlarty on dfferent tems and analyzng the nfluences of the effecteness on the ratng predcton, rather than addressng a combned model to get the optmal recommendaton as we hae learnt from the Netflx Prze competton [3]. We dscuss how to mplement our method by amendng a normal algorthm, whch wll be called orgnal algorthm herenafter. So, we wll compare 176

6 our method wth the orgnal algorthm whch s the PCC algorthm n ths paper. Snce the c-precson polcy s a weak method for mprong the predcton accuracy, we manly compare the orgnal PCC algorthm wth our Correcton Polcy and r- Precson Polcy n ths experment. The ealuaton on the MoeLens and the Netflx datasets s done based on allbut-one polcy. For the MoeLens dataset, we set 0.4, 0. as suggested by the aboe results and ary the number of test users from 50 to 00. For the Netflx dataset, we set 0.8, 0. and ary the number of test users from 100 to 300. Note that the test users and the tranng users come from dfferent user sets and the detals hae been explaned n Secton 4.1. The results are shown n Tab.1 and Tab. respectely. TABLE I. MAE ON MOVIELENS DATASET FOR THE COMPARED ALGORITHMS 50 users 100 users 00 users PCC algorthm Correcton Polcy r-precson Polcy TABLE II. MAE ON NETFLIX DATASET FOR THE COMPARED ALGORITHMS 100 users 00 users 300 users PCC algorthm Correcton Polcy r-precson Polcy We note that the performance of dfferent algorthms would mproe along wth the ncrease of the test user numbers. Ths s because, wth a larger user database, t wll be easer to fnd suffcent number of neghbors wth hgh smlartes to the acte user so that hs preferences can be estmated more accurately. Also, our method ges better results on the Netflx dataset, snce ths dataset hae greater number of moes and t wll be easer to fnd more smlar tems to calculate the tem-targeted smlarty. The aboe test results show that the tem-targeted smlarty can nduce more accurate precsons than the orgnal algorthm. V. CONCLUSIONS In ths paper, we propose an tem-targeted smlarty method for data serce recommendaton based on the obseraton that the effecteness of the user smlarty s subject to the predcted tem. Our method takes nto account the relatonshp of the predcted tem and the tems used by smlarty computatons. We select the PCC algorthm as the orgnal algorthm and dese two polces for computng tem-targeted smlartes: one s the correcton polcy and the other s the precson polcy. The experment results on some practcal datasets show that our method outperforms the orgnal smlarty method when predctng ratngs n recommendatons. For future work, we plan to do more research on how to apply our methodology n the calculaton of tem smlartes. We wll also try to combne our method wth some other technques and study ther usefulness to recommendatons ACKNOWLEDGMENT Ths work s supported n part by the Natonal Key Technology Research and Deelopment Program of the Mnstry of Scence and Technology of Chna under grants 01BAH19B00. REFERENCES [1] G. Adomacus and A. Tuzhln. Toward the next generaton of recommender systems: A surey of the state-of-the-art and possble extensons. IEEE Trans. on Knowl. and Data Eng., ol. 17(6), pp , 005. [] R. Bell, Y. Koren, and C. Volnsky. Modelng relatonshps at multple scales to mproe accuracy of large recommender systems. In Proceedngs of the 13th ACM SIGKDD nternatonal conference on Knowledge dscoery and data mnng (SIGKDD 07), pp , 007. [3] R. M. Bell and Y. Koren. Lessons from the Netflx prze challenge. SIGKDD Explor. Newsl., ol. 9(), pp , 007. [4] J. S. Breese, D. Heckerman, and C. Kade. Emprcalanalyss of predcte algorthms for collaborate flterng. Proc. the 14th Conference on Uncertanty n Artfcal Intellgence (UAI 98), pp.43 5, [5] J. Canny. Collaborate flterng wth pracy a factor analyss. Proc. the 5th annual nternatonal ACM SIGIR conference on Research and deelopment n nformaton retreal (SIGIR05), pp , 00. [6] M. Deshpande and G. Karyps. Item-based top-n recommendaton algorthms. ACM Trans. Inf. Syst., ol. (1), pp , 004. [7] D. Goldberg, D. Nchols, B. M. Ok, and D. Terry. Usng collaborate flterng to weae an nformaton tapestry. Commun. ACM, ol. 35(1), pp , December 199. [8] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Redl. An algorthmc framework for performng collaborate flterng. Proc. the nd annual nternatonal ACM SIGIR conference on Research and deelopment n nformaton retreal (SIGIR 99), pp , [9] T. Hofmann. Collaborate flterng a Gaussan probablstc latent semantc analyss. Proc. the 6th annual nternatonal ACM SIGIR conference on Research and deelopment n nformaon retreal (SIGIR 03), pp , 003. [10] R. Jn, J. Y. Cha, and L. S. An automatc weghtng scheme for collaborate flterng. Proc. the 7th annual nternatonal ACM SIGIR conference on Research and deelopment n nformaton retreal (SIGIR 07), pp , 004. [11] A. Kohrs and B. Meraldo. Clusterng for collaborate flterng applcatons. Computatonal Intellgence for Modellng, Control and Automaton. IOS, [1] Y. Koren. Factorzaton meets the neghborhood: a multfaceted collaborate flterng model. Proc. the 14th ACM SIGKDD nternatonal conference on Knowledge dscoery and data mnng (SIGKDD 08), pp , 008. [13] N. N. Lu and Q. Yang. Egenrank: A rankng-orented approach to collaborate flterng. Proc. the 31st Annual Internatonal ACM SIGIR Conference on Research and Deelopment n Informaton Retreal (SIGIR08), pp ,

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