Utilizing Content to Enhance a Usage-Based Method for Web Recommendation based on Q-Learning

Transcription

1 Proceedngs of the Twenty-Frst Internatonal FLAIS Conference (2008) Utlzng Content to Enhance a Usage-Based Method for Web ecommendaton based on Q-Learnng Nma Taghpour Department of Computer Engneerng Amrkabr Unversty of Technology, Tehran, Iran n-taghpour@aut.ac.r Abstract The problem of nformaton overload on the Internet has receved a great deal of attenton n the recent years. ecommender Systems have been ntroduced as one soluton to ths problem. These systems am at drectng the user toward the tems that best meet her needs and nterests. ecent studes have ndcated the effectveness of ncorporatng doman knowledge n mprovng the qualty of recommendatons. In ths paper we explot ths approach to enhance a renforcement learnng framework, prmarly devsed for web recommendatons based on web usage data. A hybrd,.e. content- and usage-based, web recommendaton method s proposed by ncorporatng web content nformaton nto a model of user behavor learned form usage data. Content nformaton s utlzed to fnd smlartes between usage scenaros,.e. users' seekng ther nformaton needs, and new recommendaton strateges are proposed that are based on ths enhanced model of user behavor. We evaluate our method under dfferent settngs and show how ths method can overcome the shortcomngs of the usage-based approach and mprove the overall qualty of recommendatons. Introducton The volume of nformaton avalable on the nternet s ncreasng rapdly wth the explosve growth of the World Wde Web and the advent of e-commerce. Whle n one hand, users are provded wth more nformaton and servce optons, on the other hand t has become more dffcult for them to fnd the rght or nterestng nformaton, the problem commonly known as nformaton overload. ecommender systems have been ntroduced as a soluton to ths problem (esnck and aran 1997). They can be generally defned as systems that gude users toward nterestng or useful objects n a large space of possble optons (Burke 2002). One popular applcaton area for recommender systems s web content recommendatons. Web recommendaton s consdered a user modelng or web personalzaton task (Ernak et al. 2004). One research area that has recently contrbuted greatly to ths problem s web mnng. Most of Copyrght 2008, Assocaton for the Advancement of Artfcal Intellgence ( All rghts reserved. the systems developed n ths feld are based on web usage mnng (Srvastava et al. 2000) whch s the process of applyng data mnng technques to the dscovery of usage patterns form web data. These systems are manly concerned wth dscoverng patterns from web usage logs and makng recommendatons based on the extracted navgaton patterns (Fu et al. 2000; Mobasher et al. 2000a). Unlke tradtonal recommender systems, whch manly base ther decsons on user ratngs on dfferent tems or other explct feedbacks provded by the user, these technques dscover user preferences from ther mplct feedbacks, namely the web pages they have vsted. More recently, hybrd methods that take advantage of doman knowledge,.e. content, usage and even structural nformaton of the webstes, have been ntroduced (Bose et al. 2006; Ernak et al. 2003; L & Zaane 2004; Mobasher et al. 2000b; Nakagawa & Mobasher 2003) and shown superor results n the web page recommendaton problem. In (Nakagawa & Mobasher, 2003) the degree of connectvty based on the lnk structure of the webste s used to evaluate effectveness of dfferent usage based technques for web stes wth dfferent structures. A new method for generatng navgaton models s presented n (L & Zaane 2004) whch explots the usage, content and structure data of the webste and addresses parallel nformaton needs of the user. Ernak et al. (2004, 2003) use the content of web pages to augment usage profles wth semantcs usng a doman-ontology. Most recently, concept herarches were ncorporated n a novel recommendaton method based on web usage mnng and optmal sequence algnment to fnd conceptual smlartes between user sessons (Bose et al. 2006). In ths paper we explot ths dea to enhance a renforcement learnng soluton, devsed for web recommendatons based on web usage data (Taghpour et al. 2007). Although the mentoned technque has shown promsng results n comparson to common technques lke collaboratve flterng and assocaton rules, an analyss of the system's performance, showed that ths method suffers from the problems commonly faced by other usage-based technques. We tackle these problems by proposng a hybrd,.e. content- and usage-based, web recommendaton method by explotng web content nformaton n the model of user behavor learned form 101

2 usage data. We devse content models for user navgaton sequence and utlze the content nformaton to fnd the regulartes and smlartes between usage scenaros,.e. users' seekng ther nformaton needs by browsng the web. New recommendaton strateges are proposed based on ths enhanced model of user behavor. Content-wse smlar usage scenaros are exploted as cases of user nformaton need, based on ther sequental nteractons wth web content, whch also ndcate what tems should be recommended to satsfy the nformaton need. Our hybrd model for the web page recommendaton problem emphaszes the flexblty of the renforcement learnng framework for ths problem and how t can be utlzed to ncorporate other sources of nformaton. We evaluate our method under dfferent settngs and show how ths method can mprove the overall qualty of web recommendatons. The organzaton of the paper s as follows: Frst, we overvew the usage-based method whch s the bass of our method. Next, we gve detaled descrptons of our hybrd methods. Afterwards we evaluate the methods and fnally comes our concluson along wth recommendatons for future work. Background In ths secton we overvew the method proposed n (Taghpour et al. 2007) that forms the bass of our new soluton. The proposed method explots enforcement Learnng (L) to make recommendatons from web usage data. We also pont out the man weaknesses of the method whch we am to overcome. Web ecommendatons Based on enforcement Learnng enforcement learnng (Sutton & Barto 1998) s prmarly known n machne learnng research as a framework n whch agents learn to choose the optmal acton n each stuaton or state they are n. The goal of the agent s to learn whch actons to perform n each state to receve the greatest accumulatve reward, n ts path to the goal state. To model the problem as renforcement learnng, they use the analogy of a game n whch the system s constantly tryng to predct the next pages of the user sesson, knowng her prevous requests and the hstory of other users browsng sessons. Usng the notons of N-Grams, each state S at tme t conssts of two sequences, ndcatng the sequence of last w vsted and w' recommended pages respectvely: s =< pt =< s w+ t w + 1 1, pt w+ 2,..., pt > (1),,..., > t w + 2 t Where P and ndcate the th vsted and recommended page n the state (Fgure 1). eward for each acton would be a functon of s' and s' where S ' s the next state. A state S ' s rewarded when the last page vsted belongs to the recommended pages lst. To completely defne the reward common metrcs normally used n web page recommender systems are taken nto account. One aspect to consder s when the vsted page was actually predcted by the system, n order to reward recommendatons that shorten the browsng sessons. Another factor commonly consdered n theses systems (Mobasher et al. 2000a; Fu et al. 2000) s the tme the user spends on a page. The common assumpton s that the more tme the user spends on a page the more nterested he probably s n that page. The rewardng can be summarzed as: Algorthm 1: Usage Based eward Functon 1: Assume δ ( s, a ) = s 2: P = s, w s = Pt + 1 s 3: If P Ø 4: For each page r n P 5: r(s,a,s') =r(s',p t+1 ) +=reward(dst( s, r),tme(p t+1 )) 6: End For 7: End If Where Dst(s, r) s the dstance of page r from the end of the recommended pages lst, and Tme(P t+1 ) s the tme user has spent on the last page of the state. As the sequence of prevously recommended pages s restrcted to a constant number w', the effect of each acton s lmted to w' next states and the system was mostly successful n recommendng pages vsted around w' steps ahead. Ths tends to lmt system s predcton ablty as large numbers of w' make the state space enormous. To overcome ths problem a modfcaton s devsed n reward functon. The basc dea s that when an acton/recommendaton s approprate n state S, ndcatng the recommended page s lkely to occur n the followng states, t should also be consdered approprate n state S -1, the actons n S -1 that frequently lead to S. Lmtatons of the Usage-Based Approach In our evaluaton of the system, we notced that although we were faced wth a rather large number of states, there were cases where the state resulted from the sequence of pages vsted by the user had actually never occurred n the tranng phase. Although not the case here, ths problem can be also due to the nfamous "new tem" problem commonly faced n collaboratve flterng (Burke 2002; Mobasher et al. 2000b) when new pages are added to the webste. In stuatons lke these the system was unable to make any decsons regardng what pages to recommend. S 1 <-,-,a> c S 2 <-,a,b> d S 3 <a,b,c> e S 4 <b,c,d> s S 5 <c,d,e> f. <-,-,-> r c <-,-,c> r d <-,c,d> r e <c,d,e> r s <d,e,s> r f Fgure 1: States and actons n the recommendaton model 102

3 Moreover, the overall coverage of the system on the webste,.e. percentage of the pages that were recommended at least once, was rather low (55.06%). Another ssue worth consderng s the fact that the mere presence of a state n the state space cannot guarantee a hgh qualty recommendaton,.e. a hgh Q-value cannot guarantee a hgh qualty recommendaton by tself. Smply put, when a pattern has few occurrences n the tranng data t cannot be a strong bass for decson makng, a problem addressed n other methods by ntroducng metrcs lke support threshold n assocaton rules (Mobasher et al. 2000b). Smlarly n our case a hgh Q-value, lke a hgh confdence for an assocaton rule, cannot be trusted unless t has strong supportng evdence n the data. Generally, there are cases when hstorcal usage data provdes no evdence, or evdence that's not strong enough, to make a ratonal decson about user's behavor. Ths s a problem common n recommender systems that have usage data as ther only source of nformaton. Note that n the descrbed settng, pages stored n the sequence of each state S are treated as tems for whch the only nformaton avalable s ther d. The system reles solely on usage data and thus s unable to make any generalzaton. One common soluton to ths problem s to ncorporate some semantc knowledge about the tems beng recommended, nto the system. In the next secton we descrbe our approach for adoptng ths dea Explotng Content Implcatons of the Usage- Based Navgatonal Model Motvaton In the usage-based model of the problem, each state models the nformaton need of the user as the (sub)sequence of web pages vsted by the user and the nterest he has shown n each page, e.g. by the tme spent vstng the page. Snce no addtonal nformaton besde the page d s used n the sequence, ths method fals to recognze any new sequence of page vsts and consequently fals to make predctons. The same s true regardng the acton/recommendatons, as the system would only be able to recommend the exact pages t has seen before. In ths secton we wll elaborate our approach to address these ssues based on usng the content of the pages n each state. We devse content models for the states and explot these models to fnd the states representng smlar user nformaton needs. Havng ths knowledge, we wll be able to make use of the learned user behavor n satsfyng smlar nformaton needs. For ths purpose we devse new recommendaton strateges that make use of clusters of smlar states and the approprate recommendatons, for those states to derve a new aggregate score for predctons, n a k-nn fashon. The basc dea s that whenever the user browsng results n a state S x whch has been vsted no or few tmes before, the system would fnd a set Sm k (S x ) contanng the k most smlar states to S x and compute new recommendaton scores for each canddate web page r by utlzng the scores,.e. Q-values, of recommendng r n each state S Sm k (S x ). For example the aggregaton can be done by a weghted sum of the Q-alues, such as: Score ( S x, r) = S Sm k ( S x ) W ( S ) Q( S, r) S Sm k ( S x ) W ( S ) In the followng sectons we wll present dfferent strateges for the recommendaton phase. Smlarty between the States Fndng smlartes between states s not a trval task. Frst, we need to model the content nformaton encapsulated n the state. Any state contans a fxed number (w) of sequental page vsts by a user. The content nformaton of ths sequence can be modeled n varous ways e.g. w bag of words, a vector aggregatng all page contents n a state, etc. We present two approaches to model the content nformaton of the states n attempt to capture a model of user nformaton need based on her traversal on the webste: Content Sequence Model (CSM). In ths approach we explot varous sources of nformaton about each web page p j n the sequence, combne ths nformaton nto an aggregated vector PC(p j ) n the vector space model and fnally derve a content model SC(S ) for each state S as an ordered set of these w vectors. The content vector of each web page s computed by combnng the content of the web page, the terms n the UL of the page and the anchor text of the hyperlnks pontng to the web page InLnk(p j ). Ths model s adopted from (Ernak et al. 2003): PC( p ) = α. Content( p ) + β. InLnk( p ) + γ. UL( p )) (3) j j j j = ( t 1, t2, t3,..., tn), where α + β + γ = 1 SC S ) =< PC ( p ), PC ( p ),..., PC ( p ) > (4) ( 1 2 w Where N s the total number of terms as commonly consdered n the vector space model and the weghts t j are computed usng the tf.idf metrc. Havng defned the content model the next step s to devse the smlarty functon CSM_Sm(S,S j ). We compute ths smlarty as the weghted cosne-based smlarty (CosSm) of correspondng pages n the states. More weght s assgned to pages that appear later n the user sesson based on the assumpton that users are browsng towards ther nformaton need. The smlarty s computed as: w k = 1 CosSm( PC( p ), PC( p )) pweght( k) w k = 1 k jk pweght( k) Informaton Scent Model (ISM). Informaton Scent (Ch et. al. 2001) s a model of user nformaton need based on (5) (2) 103

4 the nformaton foragng theory. In ths model user's nformaton need s modeled usng the text of the hyperlnks followed by the user n her browsng sesson on the webste and applyng a spreadng actvaton algorthm. We refer the nterested reader to (Ch et. al. 2001) for detals of ths approach. We explot ths algorthm to acheve a model of user nformaton need based on the sequence of vsted pages n each state. The result of ths procedure s also a vector n the vector space model and the smlarty between states wll agan be computed based on the cosne of the correspondng vectors. Organzaton of Smlar States Another ssue worth mentonng s that regardless of the choce of the content model, the process of fndng smlar states has to be tmeeffcent as ths process s performed durng the onlne recommendaton generaton phase. In ths regard, the states are clustered based on ther content model n the offlne tranng phase and the search space for fndng k smlar states wll be reduced to the correspondng cluster of the gven state S x. We ncorporated our smlarty functons and modfed the Dc-tree clusterng algorthm (Wong and Fu 2000) for formng state clusters n our experments. Ths algorthm s an ncremental herarchcal clusterng algorthm specfcally devsed for fndng clusters of web pages. The ncremental feature of the algorthm specfcally desred as t enables us to accommodate new states nto the approprate cluster when needed. ecommendaton Generaton The performance of the system reles heavly on the recommendaton strategy. We propose two approaches for web page recommendaton by explotng the content nformaton of the navgaton model, each wth dfferent motvatons. ecommendaton based on an Aggregate of Smlar States (ASS). Ths strategy s analogous to the approach mentoned as the motvaton. Here, we explot the fact that the usage-based model generates accurate recommendatons when t s provded wth suffcent usage data (Taghpour et al. 2007). So, whenever the user sesson results n a state S u whch has been frequently vsted by prevous users, recommendatons are made solely based on the values of Q(S u,a) for dfferent actons. On the other hand, the usage-based method loses ts accuracy when faced wth less frequently vsted sequence of pages s completely useless when faced wth new sequences. So, whenever S u s a new or rarely vsted state, an aggregate predcton of smlar states wll be exploted for recommendaton. The pseudo code n Algorthm 2 summarzes the procedure. If S u s a new state the correspondng content model SC(S u ) of the state s computed. Ths model s then compared to the mean content vector of the state clusters and the nearest cluster s found based on Equaton (5). In ths smlarty computaton, pweght(k) s also dependant to the tme the user has spent on each pages p uk, assumng pages the user has spent more tme on as stronger ndcators of her nterest. In the last step recommendaton scores for canddate recommendatons/actons wll be computed usng the nearest states S Sm k (S u ) n the selected cluster and by applyng Equaton (2). Note that n ths step we tend to assgn more weght (W(S )) to the states whch have occurred more frequently n the usage data. The whole procedure s the same where S u resdes n our state space but t has been vsted less than a gven threshold. The only dfference s that n ths case we know the cluster the state belongs to n advance and there's no need to compute the correspondng content model and fnd nearest clusters. Algorthm 2: ASS 1: Assume Current State s S u 2: If (S u s not a new state) 3: If Usage_Support(S u ) > mn_sup 4: eturn BestActons(S u ) 5: Ext; 6: Else 7: C FndCluster(S u ) 8: End If 9: Else 10: Buld SC(S u ) 11: C ClosestCluster(SC(S u )) 12: End If 13: Sm k (S u ) FndSmlar(C, SC(S u ),k) 14: For each Acton r 15: Compute Score(S u,r) usng Equaton (2) 16: Store Score(S u,r) n a set ecs 17: End For 18: eturn BestActons(ecs) Usng ths strategy, we wll be able to fnd portons of sessons whch are both semantcally smlar to our sesson, n sense of the user nformaton need, and have a rather stronger usage support. Then we recommend a combnaton of actons approprate for those states. We expect to be able to cover a hgher porton of user sessons and also gan hgher recommendaton coverage on the web pages. Although ths method manages the new sequences (even those contanng new pages added to the webste) t stll fals to recommend those newly added pages (at least before a deal of expermentaton). Inferrng a Content-based Model for ecommendatons from Smlar States (CMSS). In ths strategy, agan we explot the Q-alues of actons n smlar state to a gven state S u, but ths tme nstead of usng the actons as they are,.e. predcton of page ds, we derve an aggregate content model predctng the content of pages that mght satsfy user nformaton needs. The procedure s bascally smlar to Algorthm 2. Agan a set ecs s found contanng the best actons from states n Sm k (S u ). Afterwards, the content model of the top m pages, those wth hgher Score(S u,r) n ecs, wll be used to derve an aggregate content model of pages that mght satsfy the user's nformaton need. Then ths content model (ecq), as a weghted vector of terms, wll be used as a query on pages of the webste. The ranked web pages retreved for the query wll then be used as the recommendatons to be 104

5 presented to the user. ecq s computed as normalzed weghted sum of the content vectors of pages n ecs: e cq = r Top m (e cs ) PC ( r ) Score ( S r Top m (e cs ) Score ( S u, r ) u, r ) Ths strategy s also a hybrd of usage and content-based approaches, and t dffers from content-based recommendatons where content smlar to the tems accessed so far by the user s retreved for recommendatons. Here, the prevous usage patterns are exploted and the content query used for recommendatons s based on a predcton of the content that would follow the sequence of web pages accessed by the user. An mportant feature of ths strategy s the possblty of recommendng new pages added to the web ste, as the content queres would be evaluated aganst all the web pages, ncludng the new pages or even pages wth dynamc content. Expermental Evaluaton Expermental Settng We evaluated system performance n dfferent settngs descrbed above. As our evaluaton data set we used the web logs of the unversty webste. Ths dataset contans sessons and about 680 pages. 60% of the data set was used as the tranng set and the remanng was used to test the system. For our evaluaton we presented each user sesson to the system, and recorded the recommendatons t made after seeng each page the user had vsted smlar to the orgnal approach presented n (Taghpour et al. 2007). We used the metrcs proposed n (Bose et al. 2006) for evaluaton. These metrcs are: Ht ato (H): Percentage of Hts n recommendaton lsts, Predctve Ablty (PA): Percentage of pages recommended at least once, Clck educton (C): average percentage of pages skpped because of recommendatons and ecommendaton Qualty (Q): average rank of a correct recommendaton n the lst. Senstvty to Parameters Dfferent choce of parameters would result n dfferent types of system performance. mn_sup (Algorthm2) would determne when recommendatons for the exstng states are made based on the usage-based approach, and when smlar states would be exploted. We evaluated the usagebased method and analyzed the relatonshp between the frequences of each state's sequence n tranng sessons and the average accuracy of recommendatons made for the state. These results showed sgnfcance reducton of accuracy for states wth support values lower than 0.31%, the rato that we chose as the mn-sup threshold. Another parameter s k the sze of the S x neghborhood Sm k (S x ). We evaluated the system wth dfferent k values as shown n Fgure 2. We have a local optmum for k n both strateges (k=15 for ASS and k=20 fro CMSS). Increasng k at lower (6) value ranges mproves the performance by brngng n the knowledge stored n smlar states. After reachng a local optmum the performance stays rather flat as we obtan an almost fxed set of best actons, wth hghest Score(S u,r). Increasng the neghborhood beyond 30 seems to brng nose nto the model whch hurts the performance n spte of the dampenng effect of low W(S ) values for the less smlar neghbors. Average Ht ato ( H) Avg. Predctve Ablty (PA) ASS CMSS Neghborhood Sze (k ) ASS CMSS UBL Neghborhood Sze (k ) Fgure 2: Effect of Parameter k on Ht ato (Top) and Predctve Ablty (Bottom) of the Method Comparson to Other Methods We compared our proposed hybrd methods wth the prevous usage-based (UB-L) and a content-based approach that uses the nfo-scent model to recommend pages (CIS). Note that UB-L had shown superor results than common usage-based methods (Taghpour et al. 2007). Combnaton of the chosen strategy (ASS/CMSS) and content model (CSM/ISM) result n 4 dfferent methods. The results presented here are based on havng a maxmum of 7 recommendatons n each stage. We also expermented wth 5 and 10 as the thresholds whch resulted n the same relatve performance of the methods. As show n Table 1, the ASS-CSM out performs the rest of the methods (ncludng the accurate UB-L method) n sense of H, whle also achevng hgh levels of coverage on the webste. We can see how all our hybrd methods manage to dramatcally outperform UB-L n sense of PA and farly compete wth the, not so accurate, content-based CIS method. Both of the methods based on the CMSS strategy acheve hghest levels of PA (and C), due to the dversty of the content queres. The much hgher H values of these methods compared to the pure contentbased CIS approach could be consdered as evdence n support of the mportance of actual usage patterns n accurate nference of user nformaton needs and behavor. 105

6 Table 1: Comparson of dfferent recommendaton methods Sesson wndow sze w=3, mn_sup=0.31%, k=15 (ASS), k=20 (CMSS) Method Metrc H PA C Q PA-N UB-L CIS ASS-CSM ASS-ISM CMSS-CSM CMSS-ISM Fnally, we can see that the more elaborate CSM content model results n relatvely better performance than the ISM approach, but the margnal dfference proves the accuracy of the ISM approach, whch s favorable due to less computaton load, especally for the onlne recommendaton phase. We also evaluated the ablty of the methods to recommend new pages, omtted from tranng sessons n another smulaton, shown by the evaluaton metrc PA-N. It can be seen that the CMSS strateges manage to acheve a hgh coverage on these pages whle other approaches fal, as expected. Concluson and Future Work We presented methods to enhance, and overcome the restrctons, of a usage-based web recommender system based on L. We dentfed and tackled the weaknesses that a usage-based method nherently suffers from and ncorporated content nformaton regardng the usage patterns to mprove the system. Our evaluaton results emphasze the flexblty of the L paradgm to combne dfferent sources of nformaton n order to derve predctons of future content needs and mprove the qualty of recommendatons. Ths work can be extended n varous dmensons. One possblty s a recommendaton method that uses a combnaton of recommendatons generated by the two strateges, n order to beneft from each,.e. better accuracy of the ASS, dversty of CMSS and ts ablty to accommodate new pages. Another opton s usng a content-based, or a hybrd, reward functon n the L model whch consders the smlarty of pages n to the vsted page. Integraton of other sources of doman knowledge e.g. webste topology or a doman-ontology nto the model can also be another future work for ths paper. Fnally, we propose ntegraton of some long lastng characterstcs of the user, e.g. her long-term nterests or a model of her browsng strategy, nto the state model. eferences Bose, A., Beemanapall, K., Srvastava, J., Sahar, S Incorporatng concept herarches nto usage mnng based recommendatons. Proc. 8th WEBKDD workshop. Burke, Hybrd recommender systems: Survey and experments. User Modelng and User-Adapted Interacton. 12: Ch, E. H., Proll, P., Ptkow, J Usng nformaton scent to model user Informaton Needs and Actons on the Web. Proc.s of Human Factors n Computng Systems. Ernak, M., azrganns, M., arlams, I SEWeP: Usng Ste Semantcs and a Taxonomy to Enhance the Web Personalzaton Process. Proc. of the 9th ACM SIGKDD Conference. Ernak, M., Lampos, C., Paulaks, S., azrganns, M Web Personalzaton Integratng Content Semantcs and Navgatonal Patterns. Proc. of the sxth ACM workshop on Web Informaton and Data Management. Fu, X., Budzk, J., Hammond, K. J Mnng navgaton hstory for recommendaton. Proc.of 4th Internatonal Conference on Intellgent User Interfaces. L, J., Zaane, O Combnng usage, content and structure data to mprove web ste recommendaton. 5th Internatonal Conference on Electronc Commerce and Web. EC-WEB. Mobasher, B., Cooley,., Srvastava, J Automatc personalzaton based on web usage mnng. Communcatons of the ACM. 43 (8), pp Mobasher, B., Da, H., Luo, T., Sun, Y., Zhu, J Integratng web usage and content mnng for more effectve personalzaton. In Proc. of Internatonal Conference on Electronc Commerce and Web. EC-WEB. Nakagawa M., Mobasher, B A Hybrd Web Personalzaton Model Based on Ste Connectvty. Proc. of 5th WEBKDD workshop. esnck, P., aran, H ecommender Systems. Communcatons of the ACM, 40 (3): Srvastava, J., Cooley,., Deshpande, M., Tan, P.N Web Usage Mnng: Dscovery and Applcatons of Usage Patterns from Web Data. SIGKDD Exploratons, 1: Sutton,.S. Barto, A.G enforcement Learnng: An Introducton, MIT Press, Cambrdge. Taghpour, N., Kardan, A., Shry Ghdary, S Usage- Based Web ecommendatons: A enforcement Learnng Approach. Proc. of the ACM 2007 ecommender Systems Conference. Wong, W. and Fu, A Incremental document clusterng for web page classfcaton. Proc. Internatonal conference on Informaton Socety n the 21st century: Emergng Technologes and New Challenges. 106