A neuro-fuzzy collaborative filtering approach for Web recommendation. G. Castellano, A. M. Fanelli, and M. A. Torsello *

Transcription

1 Internatonal Journal of Computatonal Scence (Prnt) (Onlne) Global Informaton Publsher 27, Vol., No., A neuro-fuzzy collaboratve flterng approach for Web recommendaton G. Castellano, A. M. Fanell, and M. A. Torsello * Department of Informatcs, Unversty of Bar, Va Orabona, Bar, Italy {castellano,fanell,torsello}@d.unba.t Abstract. Due to the growng varety and quantty of nformaton avalable on the Web, there s urgent need for developng web-based applcatons capable of adaptng ther servces to the needs of the users. Ths s the man ratonale behnd the flourshng area of Web recommendaton, that fnds n Soft Computng technques a vald tool to handle uncertanty n web usage data and develop web-based applcatons talored on users preferences. In ths context, we propose a neuro-fuzzy strategy that combnes soft computng technques to develop a Web recommendaton system that dynamcally suggests nterestng URLs for the current user. As a prelmnary step, user access logs are analyzed to dentfy user sessons. Then, groups of users whch exhbt a common browser behavor (.e. user profles) are dscovered by applyng a fuzzy clusterng algorthm to the user sessons. Fnally, a knowledge extracton process s carred out to derve assocatons between user profles and relevant Web pages to be suggested to users. In partcular, a hybrd approach based on the combnaton of the fuzzy reasonng and the connectonst paradgm s proposed n order to derve knowledge from sesson data and represent t n the comprehensble form of fuzzy rules. The derved knowledge s ultmately used to dynamcally suggest lnks to Web pages udged nterestng for the current user. Keywords: Web Personalzaton, Web usage mnng, Web recommendaton, Clusterng, Neuro-fuzzy. * Correspondng Author. Tel.: , Fax: , Emal: torsello@d.unba.t. 27

2 Internatonal Journal of Computatonal Scence Introducton The growng quantty of nformaton and applcatons avalable on the World Wde Web has recently mposed some knd of personalzaton for the Web nformaton space []. Web personalzaton may be useful n several contexts to offer a varety of functonaltes, such as customzaton, task performance support, personalzed gudance, etc. In partcular, personalzaton gudance allows to assst the user n gettng quckly the nformaton he s seekng n a ste, wthout askng for t explctly. One man example of gudance functons s represented by lnk recommendaton whch conssts n the suggeston of a set of lnks to pages of a ste accordng the preferences and the necesstes of users [2]. Generally speakng, Web recommendaton may be ntended as the process of meetng or predctng the nterests of users by provdng them wth the nformaton or servces that they need [3], [4]. The scentfc lterature ncludes a varety of technques that have been employed to perform Web recommendaton [3]. Partcularly, three man approaches can be dentfed: content-based flterng, rulebased flterng and collaboratve flterng. Among these, the most wdely adopted s collaboratve flterng [4], [5], [6], [7], [8] whch s based on the dea to match the preferences of a current user about specfc obects (e.g. web pages) wth those of smlar users, n order to produce recommendatons on other obects not yet rated by the current user. In a Web recommendaton system, two prncpal tasks can be dstngushed: the dscovery of knowledge about the user s preferences by analyzng Web data and the effectve recommendaton process. Typcally, to execute the task of knowledge dscovery, Web Usage Mnng (WUM) methodology s employed. WUM conssts n explotng statstcal and data mnng technques n order to derve patterns of user navgatonal behavor startng from Web usage data [9], []. In the effectve recommendaton process, the extracted knowledge s used to provde recommendatons to the users, such as addng hyperlnks to the last Web page requested by the user, dependng on the type of user. Web usage data are characterzed by vagueness and mprecson. The use of tradtonal machne learnng technques n WUM may often result neffcent and nadequate to handle the uncertanty underlyng data about Web nteractons. As consequence, Soft Computng technques (.e. fuzzy logc, neural networks, neuro-fuzzy systems, etc.) can be properly appled n order to face the mprecson and partal truths characterzng the recommendaton process []. In ths paper, we propose a Web recommendaton approach for dynamc lnk suggeston explotng Soft Computng technques as tools for WUM. Specfcally, we nvestgate the use of a neuro-fuzzy strategy to develop a collaboratve flterng approach. A fuzzy clusterng algorthm s appled to determne user profles by groupng preprocessed Web usage data nto sesson categores. Then, a hybrd approach based on the combnaton of the fuzzy reasonng wth a neural network s employed n order to derve fuzzy rules useful to provde dynamcal predctons about Web pages to be suggested to the current user, accordng to the user profles prevously dentfed. The rest of the paper s organzed as follows. In Secton 2 the methodology underlyng the proposed recommendaton approach s formulated and the nvolved tasks are descrbed. In Secton 3 28

3 prelmnary smulaton results on a smple Web ste are reported and analyzed. Secton 4 concludes the paper. 2 The Web recommendaton approach The proposed Web recommendaton approach nvolves a number of tasks, as llustrated n Fg.. Specfcally, two man tasks are performed: User Proflng and Recommendaton. User proflng s the task of dscoverng a number of user categores startng from sesson data derved by preprocessng log fles. Precsely, the dentfed user sessons are used to create models of vstor behavor that are successvely grouped nto user profles by a fuzzy clusterng strategy. Startng from the extracted user profles and the avalable sesson data, a knowledge base expressed n the form of fuzzy rules s extracted va a neuro-fuzzy learnng strategy. Such knowledge base s exploted durng the recommendaton task to dynamcally suggest lnks to Web pages udged nterestng for the current user. In the followng subsectons, we descrbe n more detal all the tasks nvolved n the proposed approach. User Proflng Modelng vstor behavor Preprocessng log data Extractng user profles by fuzzy clusterng Log Creatng recommendaton rules by neuro-fuzzy learnng Recommendng lnks Recommendaton Fg.. Workng scheme of the proposed Web recommendaton approach 29

4 Internatonal Journal of Computatonal Scence 2. Modelng the vstor behavor from preprocessed log data The frst step of the user proflng task s amed to derve a model of the vstor behavor. To acheve ths, we properly preprocess log data representng all the requests made by the vstors of a Web ste. Log data preprocessng leads to dentfy a number of sgnfcant user sessons that can be useful for modelng the user navgatonal behavor. Preprocessng of access log fles s performed by means of LODAP (LOg DAta Preprocessor), a software tool that we have presented n [2]. The tool analyzes usage data stored n log fles to produce statstcs about the browsng behavor of the users vstng the Web ste. Partcularly, LODAP structures the requests made by the connected users nto user sessons by dentfyng the sequence of pages accessed by each vstor. LODAP preprocesses log data nto three steps: data cleanng, data structuraton and data flterng. Durng data cleanng, Web log data are cleaned from the useless nformaton n order to retan only records correspondng to the explct requests of the users that can be effectvely exploted to derve models of the user navgatonal behavor. Precsely, LODAP removes records related to faled and corrupted requests, records of requests for multmeda obects (such as mages, vdeos, sounds, ecc.) and records correspondng to vsts made by Web robots. After data cleanng, only nformaton concernng accesses to pages of the Web ste are retaned. We formally defne the set of such pages as P = { p, p2,..., p np }. Data structuraton dentfes user sessons by groupng the unstructured requests made by a same user for dfferent pages. To extract user sessons, the dentfcaton of dstnct users s a problem whch has to be addressed. For Web stes requrng user regstraton, dfferent users can be dentfed by explotng the nformaton concernng the user logn contaned n log fles. When the user logn s not avalable, LODAP smply consders each IP address as a dfferent user (beng aware that an IP address mght be used by several users). The set of all users (IP) s defned by U = { u,,..., u2 u nu } and a user sesson s defned as the set of accesses orgnatng from the same user (IP) wthn a predefned tme perod. Formally, a user sesson s represented as a trple s = u, t, p where u U represents the user dentfer, t s the total tme access of the -th sesson, p s the set of all pages requested durng the -th sesson. More n detal, p = ( p, t, N ), ( p2, t2, N 2 ),..., ( pn, tn, N n ) where p k s the k-th URL vsted durng the -the sesson, t k s the total access tme to page p k and N k represents the number of accesses to page p k durng the -th sesson. Summarzng, after data structuraton, a collecton S = s, s2,..., s n s of n s sessons s dentfed from the log data. Fnally, LODAP executes a page flterng and a sesson flterng process n order to retan only the most vsted pages and the most sgnfcant user sessons. Page flterng elmnates two knds of requests: requests for very low support URLs,.e. requests to pages whch do not appear n a suffcent number of sessons, and requests for very hgh support URLs,.e. requests to pages whch appear n nearly all sessons. Sesson flterng removes all user sessons that nclude a very low number of vsted URLs. Hence, after data flterng, only m page requests (wth m np ) and only n sessons (wth n ns ) are retaned. 3

5 Once user sessons have been dentfed by LODAP, vstor behavor models are created by defnng a measure expressng the nterest degree of the users for each vsted page durng a sesson. In lterature several measures have been consdered to estmate how much the user s nterested n a page of the Web ste [3], [], [4]. In our approach, we measure the nterest degree for a page as the average access tme on that page. Precsely, the nterest degree for the -th page n the -th user sesson s defned as: t ID = N where t s the overall tme spent by the user on the -th page and N s the number of accesses to that page durng the -th sesson. Hence, we model the vstor behavor of each user through a pattern of nterest degrees for all pages vsted by that user. Snce the number of pages vsted by dfferent users may vary, vstor behavor patterns may have dfferent dmensons. To obtan a homogeneous behavor model for all users, we translate behavor patterns nto vectors havng the same dmenson equal to the number m of pages retaned by LODAP after page flterng. In partcular, the behavor of the -th user ( =,..., n ) s modeled by a vector b = b b,..., b where ( ), 2 m ID f page p s accessed n sesson s b = otherwse Summarzng, we model the vstor behavors by a n m matrx B = [ b ] where each entry represents the nterest degree of the -th user for the -th page. Based on ths matrx, vstors wth smlar preferences can be successvely clustered together nto user profles, as descrbed n the followng subsecton. 2.2 Extractng profles of vstor behavor The second step of the user proflng task concerns the extracton of a number of user profles by applyng a clusterng process to the matrx of nterest degrees prevously derved. Precsely, vstors exhbtng a common browsng behavor are grouped together nto the same cluster (.e. user profle). Snce user profles are rarely well separated (a user can exhbt nterests characterzng dfferent user profles), the use of tradtonal clusterng algorthms result often nadequate to extract user profles expressng the actual user behavor. Conversely, fuzzy clusterng algorthms seems to be partcularly suted n ths context snce they enable the creaton of overlappng clusters, so that users wth dfferent nterests may belong to several profles to a dfferent extent. In our approach, the well-known Fuzzy C-Means (FCM) clusterng algorthm [5] s appled n order to group behavor vectors b nto overlappng clusters representng user profles. Brefly, the FCM algorthm fnds C clusters based on the mnmzaton of the followng obectve functon: n C 2 F u α α = c b v c, α = c= 3

6 Internatonal Journal of Computatonal Scence where α s any real number greater than, u c s the degree of membershp of the behavor vector b to the c-th cluster, v c s the center of the c-th cluster. The FCM algorthm works as follows:. Intalze U = [ u c ] =,..., n c=,..., C matrx,u () ( τ ) 2. At τ -th step: calculate the center vectors V = v as 3. Update 4. If ( τ ) U accordng to: ( τ) ( τ ) U U < ε, wth < As a result, the FCM algorthm provdes: c=,..., C A fuzzy partton matrx = [ ] u c v = c = ( ),.., c c= C n α ucb = n α uc = C c b v k = b vk 2 α < ε, STOP; otherwse return to step 2. U where u c represents the membershp degree of the u c =,..., n behavor vector b to the c-th cluster. C clusters wth prototype vectors v c, c =,..., C. Each cluster prototype v c = ( vc, vc2,..., vcm) represents a user profle descrbng the typcal navgatonal behavor of a group of users wth smlar nterests. 2.3 Creatng recommendaton rules After user proflng, the proposed Web personalzaton approach nvolves a recommendaton process that employs the extracted user profles to create recommendaton rules that assocate relevance degrees of URLs to each vstor profle. Such rules represent the knowledge base to be used n the ultmate onlne process of lnk recommendaton. Each recommendaton rule expresses a fuzzy relaton between a behavor vector b = ( b, b2,..., bm ) and relevance of URLs n the followng form: IF ( b s A k ) AND AND ( b m s A mk ) THEN (relevance of URL s y k ) AND AND (relevance of URL m s y mk ) for k =,.., K where K s the number of rules, A k ( =,, m ) are fuzzy sets wth Gaussan membershp functons defned over the nput varables b and y k are fuzzy sngletons expressng the relevance degree of the th URL. The man advantage of usng a fuzzy knowledge base for recommendaton s the readablty of the extracted knowledge. Actually, fuzzy rules can be easly understood by human users snce they can be expressed n a lngustc fashon by labellng fuzzy sets wth lngustc terms such as LOW, 32

7 MEDIUM, HIGH. Hence, a fuzzy rule for recommendaton can assume the followng lngustc form: IF (the degree of nterest for URL s LOW) AND AND (the degree of nterest for URL m s HIGH) THEN (recommend URL wth relevance.3) AND AND (recommend URL m wth relevance.8) In our approach, the creaton of recommendaton rules s performed through a hybrd strategy based on the combnaton of fuzzy reasonng wth a specfc neural network that encodes n ts structure the dscovered knowledge n form of fuzzy rules. The archtecture of the network (depcted n fg. 2) s composed of three layers computng respectvely: membershp degree to fuzzy sets; fullfllment degree for each fuzzy rule; nferred output. Unts n the frst layer L receve a behavor vector ( b, b2,..., b m ) and evaluate the Gaussan membershp functons representng fuzzy sets. In ths layer, unts are arranged n K groups, one for each fuzzy rule. The k-th group contans m unts correspondng to the fuzzy sets whch defne the premse part of the k-th rule. In detal, each unt n L receves the nterest degree for the -th page b, =... m and computes ts membershp value to fuzzy set A k as follows: ( b ) 2 () x k Ok = exp, =,..., m k =,..., K 2 σ k where x k and σ k are the center and the wdth of the Gaussan functon, representng the adustable parameters of the unt. The second layer L 2 contans K unts that compute the fulfllment degree of each rule. In ths layer, no modfable parameter s assocated wth the unts. The output s derved by computng the rule actvaton strength, as follows: (2) n O k = O k where x k and σ k are the center and the wdth of the Gaussan functon, representng the adustable parameters of that unt. The thrd layer L 3 provdes the outputs of the network,.e. the relevance values of the m web pages. Each output results from the nference of rules, accordng to the followng formula: = () O K = = (3) k = K O k (3) k O y (3) k k, =,..., m Connectons between layer L 2 and L 3 are weghted by the fuzzy sngletons set of free parameters for the neural network. y k that represent a 33

8 Internatonal Journal of Computatonal Scence b O b 2 O 2 K b m O m Fg. 2. Archtecture of the neuro-fuzzy network In order to learn sgnfcant recommendaton rules, the network s traned on a set of nputoutput samples descrbng the assocaton between user sessons and preferred URLs. Precsely, the tranng set s a collecton of n nput-output vectors: (, ) =,..., T= b r where the nput vector b represents the behavor vector of the -th user, and the desred output vector r expresses the relevance degrees assocated to the m URLs for the -th vstor. To compute such relevance degrees, we explot nformaton embedded n the profles extracted through fuzzy clusterng. Precsely, for each behavor vector b we consder ts membershp values { u c } n the fuzzy partton matrx U. Then, we dentfy the two top matchng profles c=,..., C c, c2 {,.., C} as those wth the hghest membershp values. The relevance degrees n the output 2 m r = r, r,..., r are hence calculated as follows: r = u v + u v for =,..., m and vector ( ) n c c c c2 =,..., n. Once the tranng set has been constructed, the neural network can enter the learnng phase to extract the knowledge embedded nto tranng set and represent t as a collecton of fuzzy rules. The learnng s artculated n two steps. The frst step s based on an unsupervsed learnng, based on a rval penalzed mechansm, whch provdes a clusterng of the behavor vectors and the defnton of an ntal fuzzy rule base. In ths step, the structure and the parameters of fuzzy rules are dentfed. Successvely, the obtaned knowledge base s refned by a supervsed learnng process. Here, fuzzy rule parameters are tuned va supervsed learnng to mprove the accuracy of the derved knowledge. Maor detals on the algorthms underlyng the learnng strategy can be retreved n [6]. 34

9 2.4 Recommendng lnks The ultmate task of personalzaton s the onlne recommendaton of lnks to Web pages udged nterestng for the current user of the Web ste. Specfcally, when a new user accesses the Web ste, an on-lne module matches hs current partal sesson aganst the fuzzy rules currently avalable n the knowledge base and derves a vector of relevance degrees by means of a fuzzy nference process. Formally, when a new user has access to the Web ste, an actve user s current sesson s created n the form of a vector b. Each tme the user requests a new page, the vector s updated. To mantan the actve sesson, a sldng wndow s used to capture the most recent user s behavor. Thus the partal actve sesson of the current user s represented as a vector b = ( b,..., bm ) where some values are equal to zero, correspondng to unexplored pages. Based on the set of K rules generated through the neural learnng descrbed above, the recommendaton module provdes URL relevance degrees by means of the followng fuzzy reasonng procedure: () Calculate the matchng degree of current behavor vector b to the k-th rule, for k =,.., K by means of product operator: n μk ( b ) = = μ k ( b ) (2) Calculate the relevance degree r for the -th URL as: r K r k k = = K k = μ μ k k ( b ) ( b ), =... m Ths nference process provdes the relevance degree for all the consdered m pages, ndependently on the actual navgaton of the current user. In order to perform dynamc lnk suggeston, the recommendaton module frstly dentfes URLs that have been not vsted by the current user,.e. all pages such that b =. Then, among unexplored pages, only those havng a relevance degree r greater than a properly defned threshold α are recommended to the user. In practce, a lst of lnks s dynamcally ncluded n the page currently vsted by the user. 3 Smulaton results and analyss To test the proposed Web recommendaton approach, a prelmnary expermental sesson has been carred out by consderng the log fles of a sample Web ste. The log fles contan user requests coverng a tme perod of two weeks, for a total of 3 requests. Frst of all, LODAP has been appled to dentfy user sessons by preprocessng the avalable log data. In data cleanng step, LODAP removed all the useless requests, such as accesses to multmeda obects, robot s requests, faled and corrupt requests, leadng to 95 sgnfcant requests. In the data structuraton step, LODAP dentfed user sessons by groupng such requests. Specf- 35

10 Internatonal Journal of Computatonal Scence cally, the requests orgnatng from the same IP address durng an establshed tme perod of 25 mnutes were grouped nto a sesson. A collecton of 25 sessons were dentfed ncludng requests for 5 dstnct pages. Then, page and sesson flterng were appled to select the most vsted pages and the most sgnfcant user sessons. In partcular, to perform page flterng, LODAP counts for each page p, the number NS of dfferent sessons that nclude a request for p. LODAP performed very low support flterng by removng all pages that satsfed NS < ε, where ε s equal to % the quantty NS = max NS. Very hgh support flterng was executed =,...,5 by deletng all pages such that NS > NS ε. In the sesson flterng step, all user sessons contanng a low number of vsted URLs were removed. Precsely, a threshold η = 4 was fxed whch represents the mnmum number of dstnct pages that a user sesson should contan to be retaned sgnfcant. Hence, sesson flterng removed all user sessons s whch satsfed the condton NP < η, where NP s the number of dstnct URLs vsted n that sesson. At the end of preprocessng, a number of 2 user sessons were dentfed and dstnct pages were retaned. For the sake of brevty, we ndcate the selected pages by the letters A, B, C, D, E, F, G, H, I and J. Once user sessons were dentfed, vstor behavor models were derved by calculatng the nterest degrees of each user for each page, leadng to a 2x behavor matrx. Next, the FCM algorthm was appled to the behavor matrx n order to obtan clusters of users wth smlar navgatonal behavor correspondng to the user profles. To evaluate the valdty of the clusterng process, two dfferent ndexes wdely used n lterature were adopted: the Dunn s ndex and the Daves-Bouldn ndex [7]. The Dunn s ndex D s defned as: where ( X, ) δ D = mn mn C C max k C ( X, X ) { ( )} Δ X δ X represents the ntercluster dstance between clusters X e Δ X k represents the ntracluster dstance of cluster X k and C s the number of clusters. The goal s to maxmze ntercluster dstances whlst mnmzng ntracluster dstances. Hence, large values of Dunn s ndex correspond to a good cluster partton. The Daves-Bouldn valdaton ndex DB s defned as: k ( X ) + Δ( X ) ( ) X, X X, ( ) where ( ) X ( ( C Δ DB = max C = δ δ X,, Δ X ), Δ X ) are defned as above. In ths case, small ndex values correspond to good clusters. We carred out several runs of the FCM by settng dfferent values of the number of clusters (C=3,, 2). To obtan more relable results, each run was repeated tmes and the average values of the valdty measures were consdered. Fgures 3 and 4 depct the average values of the Dunn s and the Daves-Bouldn ndexes for dfferent numbers of clusters. It can be seen that the best partton was obtaned wth C = 6, because t provdes the best values for both ndexes. 36

11 Based on the prototypes of the sx clusters, a collecton of sx user profles was derved. In Table, for each user profle the pages wth hghest nterest degree are ndcated. It can be noted that some pages (e.g. pages I and D) characterze more than a profle, thus showng the mportance of usng fuzzy clusterng for user proflng. The next step was the creaton of recommendaton rules startng from the extracted user profles. A neural network wth nputs (correspondng to the components of the behavor vector) and outputs (correspondng to the relevance values of the Web pages) was consdered. The nternal layer of the network contans 6 unts that compute the fulfllment degree of each rule. The network was traned on a tranng set of 4 nput-output samples derved from the avalable 2 behavor patterns and from the 6 user profles, as descrbed n Secton 2.3. The remanng 6 samples were used for testng. The tranng of the network was stopped when the error on the tranng set dropped below., correspondng to a testng error of.3. The derved fuzzy rule base was ntegrated nto the onlne recommendaton module to nfer the relevance degree of each URL for the current user. These relevance degrees were ultmately used to suggest a lst of lnks to unexplored pages retaned nterestng to the current user. To perform lnk recommendaton, the navgatonal behavor of the current user was observed durng a sldng wndow of 3 mnutes n order to derve the behavor pattern correspondng to hs partal vst. Such behavor pattern was used as nput to the fuzzy rule nference process that computes the relevance degrees for all the consdered pages. Then, among the unexplored pages, only those havng a relevance degree greater than α =.7 were ncluded n the lst of lnks to be suggested. How to dynamcally present lnk to the nterestng pages wthn the currently vsted page s an aspect stll under nvestgaton D values cluster number Fg. 3. Dunn s ndex values 37

12 Internatonal Journal of Computatonal Scence,9,8,7,6 DB values,5,4,3,2, cluster number Fg. 4. Daves-Bouldn s ndex values Table. The extracted user profles by settng C=6 User Profles Pages characterzng the profle A (.82), D(.48), I(.8) 2 C(.86), F(.83), I(.75) 3 B(.86), I(.8) 4 D(.88), G(.85), J(.82) 5 E(.88), J(.84) 6 A(.84), D(.45), H(.82) 4 Conclusons A Web recommendaton approach based on the combnaton of Soft Computng technques has been presented. We nvestgated the use of a hybrd approach onng the advantages of neural networks and fuzzy reasonng n order to develop a recommendaton system that dynamcally suggests nterestng lnks to the current user on the bass of fuzzy rules. The frst task of our Web recommendaton approach s the creaton of user profles that synthesze the nterests of users wth smlar browsng behavor. We perform ths task by a fuzzy clusterng algorthm that enables creaton of overlappng clusters, so that a user, accordng to hs browsng behavor, can belong to more than a profle wth dfferent membershp degrees. The sutable number of profles s determned by usng cluster valdty measures. The second task s the creaton of a set of fuzzy rules that assocate relevance degrees of URLs to each vstor profle. Expermental results on the sesson data of a smple Web ste showed the effectveness of the proposed approach and encourage ts applcaton to more complex Web domans. Currently, we are expermentng the approach on hgh-dmensonal sesson data to evaluate how the proposed approach scales wth the number of pages. 38

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