Personalized Information Retrieval Approach

Transcription

1 Personalzed Informaton Retreval Approach Myram Hadjoun 1, Mohamed Ramz Haddad 1, Hajer Baazaou 1, Mare-Aude Aufaure 2, and Henda Ben Ghezala 1 1 Laboratory RIADI-GDL, Natonal School of Computer Scences, Unversty of Manouba, 2010 la Manouba, Tunsa {myram.hadjoun, hajer.baazaouzghal, henda.benghezala}@rad.rnu.tn haddad.medramz@gmal.com 2 MAS Laboratory, SAP BusnessObjects Char, Ecole Centrale Pars, Grande Voe des Vgnes, Chatenay-Malabry, France mare-aude.aufaure@ecp.fr Abstract. Web nformaton system personalzaton s an emergent research feld wth the objectve to facltate the use and the control of Web content. Ths paper presents a personalzed nformaton retreval approach based on end user modellng. The proposed approach personalzes data retreval usng mplct user nformaton and nterests measurements. As the data manpulated s expressed by attrbutes and values, we defne several smlarty measures. These measurements consder both semantc and spatal user contexts. The approach personalzes Web content and especally spatal nformaton focusng on ts spatal semantc aspects. Keywords: Web personalzaton, spatal Web personalzaton, user modelng 1 Introducton The volume of nformaton avalable on Web nformaton systems s growng contnuously. Browsng ths content becomes a tedous task gven the presentaton of data that does not meet user s ams and needs. To satsfy user needs, personalzaton s an approprate soluton to provde an adaptve and ntellgent Human-Computer- Interacton (H.C.I) and to mprove the nformaton systems usablty. Moreover, Web resources ncreasngly contan geo-referenced enttes generally assocated wth a geographcal locaton [1]. Geographc nformaton systems suffer from a lack of data qualty snce the presented data s hghly complex and dverse. Manpulated objects have a very rch semantc and the degrees of user s nterests towards them vary dependng on context and on personal tastes. Exstng personalzaton approaches try to determne user preferences to help hm whle explorng nformaton. However, these approaches have some drawbacks and generally neglect semantc and spatal aspect of the manpulated data. The semantc Web provdes an approprate

2 Proceedngs of WISM 2009 background to defne and to descrbe nformaton systems content n order to take nto account ts dfferent semantcs and to effcently understand t. Ths paper presents a personalzed nformaton retreval approach based on end user modelng. The proposed approach personalzes data retreval usng mplct user nformaton and nterests measurements. We start, n the next secton wth related works presentaton and dscusson. We then present our archtecture ncludng user and data modelng approaches and the smlarty measures used to ncrease the qualty of the personalzaton process and the measures used to deduce user s nterest. These measures help us to construct a user s network based on our proposed system. Secton 4 presents ths network. Secton 5 concludes our work and gves some perspectves. 2 Related Works Generally, personalzaton methodologes are dvded nto two complementary processes whch are (1) the user nformaton collecton, used to descrbe the user nterests and (2) the nference of the gathered data to predct the closest content to the user expectaton. In the frst case, user profles can be used to enrch queres and to sort results at the user nterface level [2]. Or, n other technques, they are used to nfer relatonshps lke the socal-based flterng [3] and the collaboratve flterng [4]. For the second process, extracton of nformaton on users navgatons from system log fles can be used [5]. Some nformaton retreval technques are based on user contextual nformaton extracton [6]. Informaton semantcs are also used to enrch the personalzaton process; queres can be enrched by addng new propertes from the avalable doman ontologes [7]. The user modelng based on ontology can be coupled wth dynamc update of user profle usng results of nformaton-flterng and Web usage mnng technques. Wth the evoluton of the nternet, Web resources ncreasngly contan georeferenced enttes generally assocated wth a geographcal locaton [1]. Statstcs collected through search engnes show that spatal nformaton s pervasve on the Web and that many queres contan spatal specfcatons, but t s more dffcult to fnd relevant resources respondng to query ncludng a spatal component [8]. The spatal nformaton personalzaton should consder spatal propertes and relatonshps found n Web documents. Desgn of spatal Web applcatons requres at least three components: (1) a user model and assocated user preference elctaton mechansms and (2) a personalzaton engne combnng spatal and semantc crtera and (3) a user nterface enrched wth spatal components [9]. The spatal Web personalzaton requres the representaton of user features, partcularly those relevant to the spatal doman. [10] explores semantc smlarty and spatal proxmty measures as well as relevance rankng functons on the behalf of the user. Semantc smlarty s the evaluaton of semantc lnks exstng between two concepts [11]. [12] ntroduced a classfcaton algorthm for measurng spatal proxmty between two regons. Another aspect of spatal Web personalzaton technques concerns nteractve adaptve map generaton and vsualzaton. These technques are concerned wth Web maps adaptaton accordng to user s needs [13]. In a prototype

3 Proceedngs of WISM 2009 appled to martme navgaton, [14] categorzes the users accordng to ther geographcal context. The presented personalzaton approaches have contrbuted to the mprovement of nformaton systems use. However and despte ther wdespread use, these approaches have weaknesses and lmtatons. In fact, several approaches, lke the collaboratve ones, present the same recommendatons for all users wthn the same cluster. Thus, they do not consder some specfc users preferences when they represent a mnorty n a gven group. Content based approaches facltate tems retreval by proposng some alternatves and recommendng smlar tems to the one that the user s vstng. However t focuses only on the user s actual and temporary needs and can t hghlght the tems that are related to the current query results. Other approaches try to determnate the nterests of each user but they are lmted by ther tems model that doesn t descrbe the dfferences between tems propertes. Ths lack of semantc descrpton of the tems decreases the qualty of personalzaton snce smlartes and dssmlartes between tems can t be measured accurately. In addton, n most personalzaton approaches, the spatal aspect s not taken nto consderaton, whch requres an adaptaton of those approaches to be relevant whle appled to spatal nformaton. These lmtatons explan the mportance gven to hybrd approaches. The hybrdzaton of exstng approaches s presented as an alternatve that would mprove the qualty of personalzed systems [15]. 3 Personalzed Informaton Retreval System We present n ths secton a Personalzed Informaton Retreval approach for Web nformaton systems. Our proposton s based on a dynamc and teratve constructon of a multdmensonal user model. Ths multdmensonal approach s used to represent and descrbe the user towards dfferent dmensons. The model proposed (noted M u ) s composed of 4 dmensons: user profle, spatal model, graphc model and textual model. If we consder U as the set of users, a user u U wll have as model M u : M u = (1) D D D P t s n Where: D t = keywords employed by the users for ther textual search D s = spatal postons of the users D n = enttes vsted by the users P = user profle. Ths model s part of the user modelng level of our system whch s composed of the followng ones: user level, applcaton level, user modelng level, nformaton retreval level and storage level [16]. In ths paper, we only consder the modelng level whch s also concerned wth the exploraton and the dentfcaton of semantc and spatal relatonshps between enttes extracted from log fles or from real-tme navgaton.

4 Proceedngs of WISM 2009 Users can be known or unknown. A known user has an account and s ndvdually dentfed by the system whle an unknown user does not possess proper profle. So, the user modellng level s also dealng wth: For unknown users: (1) the constructon of user real-tme profle and (2) the mappng of ths current user nto the closest prototype. For known users: (1) the actvaton of the concerned user model and (2) the update of hs real-tme profle. The model s based on an mplct nteracton wth the user: mplct because the user sn t drectly asked to gve opnon, and nteractve because we use the navgaton to measure ts nterest to a gven entty. These measurements are based on: The smlartes that could exst between attrbutes and enttes of nterest. The deducton of user nterests from all ts navgaton. The calculaton of the pertnence of a supposed spatal move. In fact, n our approach, we also consder that f a user n nterested on a spatal zone, he ams to move to. Next sectons present the measurements cted above Smlarty measures The smlarty measures used n our system are attrbutes values smlarty and entty smlarty. Attrbutes values smlarty These attrbutes can be smple attrbutes lke numerc values, Booleans and strngs. Otherwse, attrbutes can be composte such as numerc ntervals and sets, or strngs sets. We defne for each attrbute type a smlarty measure: Numerc values: These values are dscrete, so t s necessary to have a smlarty functon to dvde ther varaton ntervals nto sets of smlar values such as for hotel prces or rooms number. Each value can be consdered to be smlar to neghbourhood s values. The neghbourhood s wdth ncreases when the value s hgh and vce versa. We defne neghbourhood of smlar values as: α α Sm ( α ) = β, β α, α + (2) ε ε Where ε defnes the neghbourhood s wdth. Semantc dstance between α and β [a,b] s: D sem α β α, β ) = a b ( (3) And the smlarty degree between them s: D ( α, β ) = 1 D ( α, β ) sm sem (4) Wth these three equatons, we wll have the smlarty as: Sm ( 2α ) b a ( α ) = β, D ( α, β ) 1 ε (5) sm

5 Proceedngs of WISM 2009 Numerc ntervals: The nterval attrbutes may have common values. These values are ncluded wthn the smlarty measure. If we consder A and B as two numerc ntervals, the smlarty degree between them s: D sm A B ( A, B ) = (6) A B Sets: as for ntervals, groups or lsts can have common values. Here, we need to defne the well known ntersecton and unon operators to nclude our smlarty. Let s consder Sm(F,a ) the set of all values that are smlar to the value a of the attrbute F. Intersecton and unon operators takng nto account the smlarty appled to the sets A consdered as reference, and B are: Defnton 1 A B =, s s { a A, b j B tq b j Sm ( F a )} A { b B, a A tq b Sm( F a )} A B =, j So, wth A and B as two sets of numerc values, the degree of smlarty becomes: D sm card A, B) = card { A B} s { A s j ( (7) Thus, A and B are consdered smlar f D ( A, B) ε. sm ε s the smlarty threshold to be determned n relaton to the attrbute semantc. Other types: On the cases of the other types of attrbutes lke strngs or Boolean the smlarty operator s equalty. Enttes smlarty The smlarty measure between two enttes corresponds to the aggregaton of ther attrbutes smlarty degrees. If x and y are two enttes of the same concept, γf s the attrbute s mportance coeffcent. α and β are the attrbute values submtted by x and y. Semantc dstance between them s: D sem ( α, ) n ( x, y ) = F * D β sem = 1 γ (8) Wth n: number of attrbutes descrbng x and y whch depends on the concept to whch they belong. Havng ε as the smlarty entry of the attrbute F, we consder that x and y are smlar f: n D ( x, y) γ F * ε sem = 1 So the set of smlar enttes to x s: n Sm() x = D ( x, y) 1 F * ε sm = 1 (9) γ (10) For buldng the user model, we start wth the profle constructon, takng nterests nto consderaton. Ths nformaton s deduced by measurng user nterests related to

6 Proceedngs of WISM 2009 textual, spatal and navgatonal dmensons. Next secton presents the approach used to buld the user profle usng nformaton from navgatonal dmenson User nterests The concept of mplct nformaton gatherng adopted s as follow: when a user touches nformaton, he s mplctly votng for t. Based on ths assumpton, we ntroduce measures that am to deduce user nterests. In the followng, u represents a user; an tem vsted by u s noted as x and a concept (accommodaton, restaurant, etc ) to whch x belongs s noted C. Interests ndcators The user profle, n our context, s also used to descrbe user nterest towards the vsted tems. We use the followng parameters: Vsts frequency: Represents the number of vsts to a gven tem. Ths number can reflect the user nterest for an tem or a group of tems and can be used to determne the most nterestng ones. Vsts duraton: In addton to the vsts rate, the vst duraton s also sgnfcant as users spend more tme readng nformaton perceved as relevant [1]. Explct tems ratng: User s behavours do not always reflect hs real nterest: a user can vst an tem descrpton more than once or spend much tme on readng t just to satsfy curosty. To avod such stuaton, the user can explctly evaluate hs nterest. Ths evaluaton s then used to adjust the prevous measures notes. Vsted enttes nterest The man measures used here to deduce the user nterest are the frequency and the duraton of vsts. To do ths, we use all saved user transactons and ther duraton. If the user gves an explct evaluaton on a vsted entty, we can refne these measures. The nterest degree I e of a u towards a vsted tem x, consderng only the semantc aspect of x, s: 1 d ( u, x) n ( u, x) I ( u, x) = v + v V ( U, x) e 2 D () u N () u (11) v v Where d v (u,x) : Duraton of the user vsts to x. D v (u) : Total duraton of the user vsts. n v (u,x) : Number of tmes the user u vsted x. N v (u) : Total number of user vsts u. V(u,x) : Average rate assgned by u to x. Ths note s between 0 (not nterestng) and 1 (Very nterestng). The default value s 0.5 f the user dd not rate the tem. Thus, the qualty of preferences and nterests elctaton process ncreases when the user evaluates explctly vsted enttes. Takng nto account ratngs s mportant when a user spends a long tme vstng an entty that s not nterestng for hm or when he vsts frequently an tem that he s not plannng to vst. So, ratng ths entty would reduce the nfluence of the duraton and the frequency of those vsts.

7 Proceedngs of WISM 2009 Concepts nterest Spatal enttes are classfed nto several predefned concepts wth regard to the consdered doman. Each spatal concept s defned by several attrbutes whch are used to descrbe enttes belongng to t. In order to make a personalzed and relevant classfcaton of concepts we measure the user nterest towards spatal/semantc enttes belongng to a consdered concept. Wth x C, the nterest degree of u towards C s: I c ( u, x ) Where n s the number of tems x vsted by u. I e (u,x ) : The nterest degree of u towards x. n ( u, c ) = I e (12) = 1 Features values nterest After presentng the metrc for classfyng concepts by nterests degrees, we am to order enttes wthn the same concept accordng to ther relevance. Ths s done wth correlaton to concepts,.e. the attrbute s nterest degree value s calculated by consderng the nterest to enttes wth smlar value. Consder an attrbute F C and v k one possble value of F and X(F,Sm(F,v k )) the set of all enttes vsted by the user and whch have a smlar value to v k. Sm(F,v k ) s the set of F values that are smlar to v k. The smlarty operators depend on the attrbute type and semantc. The nterest value of u the value v k s: I f ( u, F, v ) = k x j ( F, Sm( F, v ) k I e u, x j The next step s to normalze ths nterest value to be ndependent of the elements number. I v I ( U, F, v ) f k U, F, v ) = k v V ( F ) I ( U, F, v ) j f (13) ( (14) Where V(F ) s the set of all possble attrbute F values. Interest predcton As already mentoned, user preferences are ntroduced by nterests degrees on spatal/semantc concepts and ther attrbutes values are deduced from user navgatons. We use the prevous metrcs to predct the user s nterest degree for the non-vsted tems. So, we assume that: The user s nterest degree towards a concept ncreases wth the number of vsts to enttes of ths concept. The more preferred entty values are, the more ths entty s lkely to be vsted. The concepts attrbutes have dfferent mportance degrees. So we can dstngush between the crtcal attrbutes and those that do not nfluence the user choces. Ths measure s used to predct the most relevant content the user s searchng for. Moreover, t contrbutes to refne results of textual search manly based on the use of concepts and attrbutes that wll defne the doman ontology. We am to deduce ths ontology from prevous (and current) search and navgatons and not to explctly nvolve the user. To acheve these ponts, we present the followng formula, whch

8 Proceedngs of WISM 2009 predcts the degree of user satsfacton for one entty x dependng on ts concept C and ts attrbutes values: S n ( x, u) = I ( u, C) α I ( u, F, v) (15) c = 1 Where n s the number of the concept attrbutes C v s the value of the attrbutes F presented by the entty x. Ic(u,C) s the nterest degree of u on the concept C. I v (U,F,v) s the nterest degree of u on the value v of F. F v 4 Constructon of the user results Havng as objectve the ameloraton of users results, we terate the constructon of hs model. In the end of each teraton, we determne the data pertnence and use ths value n the next teraton. Wth ths hypothess, two types of nformaton are the bass of personalzaton: the locaton of user at the moment of hs search and noted L u and the locaton of hs area of nterest correspondng to the target search zone noted Z. The area of nterest s deduced from the poston on a map n the user nterface or from the search keywords. The results provded to users are constructed on the bass of three ponts: (a) the path needed to reach the area of nterest Z from the user locaton when searchng L u, (b) the arrval pont and (c) the semantc and spatal dstance between a departure pont and the objects of nterest. The area of nterest path. The estmaton of the arrval path over the area of nterest s a qualty crteron. In fact, the taken path has an mpact on the arrval pont nto the area of nterest and therefore affects the locaton of objects of nterest dsplayed to the user. In ths context, we use a qualtatve evaluaton based on an explct weghtng of crtera such as cost or type of way. Then, the user can add ts own choce value. The mnmum crtera that we have taken are the type of road, ts cost and dstance travelled. The weghts are made of values between 0 and 1, values whch are gven n the form of notes by the user. The path evaluaton equaton s: Wth α, β and λ ]0,1] EvalPath = α. Type * β. Cost * λds tan ce (16) The arrval estmaton. The move of the user from ts search locaton to ts area of nterest can provde us two types of nformaton: (1) the arrval pont nformaton deduced from the path taken when move and (2) a locaton marker that s the collapse pont of the user, we called t departure pont. Ths pont may be the subway staton closest to the object of nterest or the user resdence. The dstance between the arrval and the departure ponts s equal to 1 when these two ponts are joned. The hypotheses are: Exstence of an arrval pont n Z.

9 Proceedngs of WISM 2009 If user moves to Z, he has a departure pont and a path: d(arrval pont, departure pont) The evaluaton s based on the path and on the semantc of the searched object: ( ArrPt, Path ) = D ( ArrPt, DepPt ) Sem( SearchObje ct) (17) Eval * SemSpa Semantc and spatal dstances. These dstances are the key features to nclude the spatal aspect nto the nformaton flterng process. Semantc and spatal dstances between the departure pont DepPt and the nterest objects object are evaluated as shown below: DsemSpa n ( ArrPt, DepPt) = Dspa(object,DepPt) * Dsem(object,DepPt) (18) = 1 Where: n s the number of the nterestng objects n the area Z. Dspa(object,DepPt) s the spatal dstance between object and DepPt. Dsem(object,DepPt) s the semantc dstance between object and DepPt. In the end of every teraton, these three measures are appled to amelorate the next one. Takng nto account the area of nterest path wll explctly mplcate the user, whle the arrval estmaton and the semantc and spatal dstances calculaton s mplctly done. 5 Concluson and Future Work Web personalzaton attracts rsng research efforts to facltate Web nformaton retreval and navgaton. Generally, Web personalzaton and user modelng approaches do not focus on the spatal aspect of the nformaton and the constrants t mples. In ths paper, we have presented some background knowledge on exstng Web and spatal Web personalzaton systems. We have then ntroduced our proposton for a personalzed nformaton retreval approach whch s manly based on the end user modelng. The user model s multdmensonal and t s bult wth teratons that use estmatons calculated from mplct user s navgaton nformaton. Next step of our work s to add to ths personalzaton process (1) nteractons between the users models and (2) as a consequence, the constructon of a network of models. In fact, as we consder smlartes and dstance between the search concepts and enttes wthn ths process, we assume that addng also smlartes measurements between the users models could be helpful n the personalzaton process.

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