Research on Interest Model of User Behavior

Transcription

1 2011 Iteratioal Coferece o Computer Sciece ad Iformatio Techology (ICCSIT 2011) IPCSIT vol. 51 (2012) (2012) IACSIT Press, Sigapore DOI: /IPCSIT.2012.V51.94 Research o Iterest Model of User Behavior Yu Ye*, Guowe Wu ad Xi Luo School of Computer Sciece ad Techology, Doghua Uiversity, Shaghai , Chia, Abstract. I searchig for the websites ad portal sites, user iterest model is applied i describig the users prefereces. We summarized some popular methods of creatig user iterest model, such as creatig models i explicit or implicit ways. O the aalysis of users' browsig behaviors or cotet, we could extract the iterests of users. A improved method is proposed to icrease the umber of web pages efficacious iformatio cotaiig, improve the accuracy of user iterest model by itroduce of search tails ad so o. Keywords: User model; hybrid iterest model; VSM(Vector space model). 1. Itroductio Recetly search egie rarely orders their results based o idividual user iterests. However differet users have various iterests ad backgrouds, thus they should obtai the differet feedback for the same query. For example: cosider the same query apple, the user has iterest i life should obtai the related websites of the food, but the perso who likes digital products, hopes to get the results of Apple digital products.researchig has they should obtai the differet feedback for the same for user iterest model costructio have become a importat part of persoalized search egies. Accordig to a aalysis of report which extract from the website of hitwise(table 1),the most commo query legth submitted to search egie(20.29%) was oly two words log,65.86% of all queries were three words log or less ad 10.57% of all queries were more tha 5 words log. The percetage of keyword which cosists of four words log or more begi to rise. This suggests that short queries are ofte ambiguous. The percetage of keyword which cosisted of four words log or more begi to rise. This suggests that short queries are ofte ambiguous. I order to solve the problems, user iterest model is proposed which allows the user to obtai more precise iformatio. The cotributios of this paper are as follows. At first, we make a geeral overview of the Keyword Search ad the ecessity of capturig user s iterest. I sectio II a brief of the category of the collectio for user iterest is itroduced. I sectio III, a geeral overview of the cocept of user iterest model, VSM, hierarchical model ad tree structure model are itroduced. At last we draw a coclusio i the last sectio. 2. User Iterest Collectio User iterest model is usually defied as a set of user s goals, plas, beliefs, kowledge ad so o ad as a descriptio about ma s uderstadig of the outside world ad as a model which is used to capture user s eeds, iterest TABLE I. PERCENTAGE OF CLICKS BY NUMBER OF KEYWORDS Percetage of U.S. clicks by umber of keywords Correspodig author. address:leafrai_001@hotmail.com 562

2 Subj ect (wor d) Ja- 08 (%) Dec- 08 (%) JAN- 09 (%) Year-overyear Percet chage % % % % % % % % ad record or maage user s iterest. Iformatio of user s iterest ca be captured from relevace feedback. The relevace feedback is to take the results that are retured from user [1] Explicit Explicitly feedback is obtaied from user s direct assessmet. For example: askig for feedback such as ratigs or appetite. Oe way to get user iterest explicitly usually completed i the registratio. I order to collect iformatio we require user to fill i relevat iterest whe they logi first time. Aother way is that allow users to commet o results returig by search egie, such as ot iterest, somewhat iterest, iterest, or very iterest. The from the feedback we lear the users curret iterests ad update model of the user s for future iformatio filterig Implicit Recetly, a great deal of effort i the research commuity has focused o improvig user experiece i web search through the icorporatio of implicit user feedback [2], [3], ad [4]. User iteractio with Web search egies is ordiary, ad complicated, i iformatio-searchig process. I the course of a search, users take various behaviors to obtai cotet which they are iterested i. The fact that a user has browsed a website is actually that he is iterested i the cotet of it. Obviously we ca derive the user s iterest by classifyig iformatio from user s browsig behavior such as which documets they select for viewig, the duratio of time spet viewig a documet, or page browsig or scrollig actios ad so o. User behavior ca be summarized as the followig aspects. 1)Search keywords Accordig to chages i the user's query iput, ifer whether there has chage i user search behavior. 2)Browsig history Site visited will be recorded i the server log, icludig the user's access time, user s time zoe, the size of visited page ad other iformatio, by aalyzig server log we ca capture the user's search iterest. 3)Bookmark Typically, users will save their favorite web page for the ext visit; therefore, we ca obtai the user s iterest by aalyzig the bookmarks. 4)Mouse behavior Users would do some operatios o the lik that they are iterested i. For example: drag, click, suspeded ad so o. We ca obtai iformatio by aalyzig these operatios. 5)Usig page dwell time Dwellig o a page for a sigificat amout of time implies that the user is iterested i it. There has more importat iformatio i the web page. I fact, previous studies have show that a dwellig time of 30 secods or more o a web page implies a kik of iterest [5]. 6)Search paths 563

3 Search paths are a series of pages startig with a query ad edig with a evet like closig the browser. Search results ca oly be used as the startig poits for collectio of user iterests. A search path cosists of a origi page, itermediate pages, ad a destiatio page [6]. Origi: The first page i the paths of the SERP (search egie result page). P2 is the origi i Fig.1. Destiatio: The last page i the paths, before visited should browse a series of itermediate pages. P5 is the destiatio i Fig.1. Origi ad destiatio is regarded as the core pages that ca reveal user s iterest. Q1 P2 P3 P4 P3 P2 P5 Fig. 1: Web behavior graph illustratig a search trail 2.3. User Iterest Model Costructio User iterest model is a collectio of persoal data associated to a specific user. It ca also be cosidered as the computer represetatio of a user iterest model. Traditioal process of creatig user iterest model is Fig.2. User iterest model has a short-term iterest model ad log-term iterest model. The short-term iterest model used to store user s recet iterest ad the stable iterests stored i log-term iterest model. The available data of user s iterest which represeted by the vector collected from registratio iformatio filled by user whe his first logi. The we adjust ad build short-term iterest model by clusterig ad aalyzig the iformatio. Usually we use the vector space model to represetig user iterest model. I additio, we ca capture user s iterest by aalyzig log which iclude user behavior ad browsig history. This is a implicit way to get user iterest. We ca coclude that the short-term iterest should be marked as the log-term iterest whe the frequecy of usig the short-term iterest reached a threshold. 3. USER INTEREST MODEL There are maily tow ways to describe the user iterest model: Vector Space Model (VSM) ad Hierarchy Model [7]. Salto's Magic Automatic Retriever of Text cotais a similar vector space model, Iverse Documet Frequecy (IDF), the term frequecy (TF), term discrimiatio values ad related cocepts such as feedback mechaisms [8] VSM Vector space model is a mathematical model which represets the feature vectors of a documet. The basic of VSM is that a documet ca be represeted by a vector. (W 1,1,W 2,2,W 3,3,..,W i,i ), W i is the weight of the i-item. There have some differet ways to computig these values. Oe of the best methods is TF-IDF (term frequecy-iverse documet frequecy) weightig. Geerally we choose sigle words, words or loger phrases as the feature item of the documet. At first, Salto used a fuctio of umber of properties that are assiged to differet documets to computig the similarity betwee both documets [8]. W i = (W i1, Wi2 W ik ) W j = (W j1, Wj2 W jk ) Followig fuctios are ecessary for similarity computig. (1) is the sum of the weights. k 1 W ik 564

4 jk W ik * k 1 W (2) (2) is the sum of the correspodig term weights for W ik ad W jk k 1 mi( W ik, W jk) (3) is the sum of the miimum weights. Ad the Dice ad Jaccard compute the similarity i documets by followig coefficiets [8]. 2[ ik jk k1 sim 1(W, i W) j (4) k1 (W * W )] Wik k1 W jk mi(w, W ) ik jk k1 sim 2(W, i W) j (5) Relevacy rakigs of documets ca be calculated by calculatig the similarity of the documet. Usually we calculate the agle betwee vectors of differet documets. Istead of the agle, we calculate the cosie of the agle betwee the vectors. v *v cos v1 * v2 k1 Wik 1 2 TF-IDF weights (term frequecy-iverse documet frequecy) TF-IDF is the commo weightig techiques i iformatio retrieval. The model is kow as term frequecy-iverse documet frequecy model. (3) Wt, tf t is term frequecy of term t i documet d. We have the term frequecy, defied as follows: d D tft *log {t d} tf i, i, j k j k, j Where i,j is the occurrece umber of the term (t i ) i documet d j ad the deomiator is the sum of umber of occurreces of all terms i documet d j. D log {t d} is iverse documet frequecy. D is the total umber of documets i the corpus. The deomiator is the umber of documets where the term t appears Tree structure A tree structure is a way of represetig the hierarchical ature of a structure i a graphical form. Usually people use a tree structure model which with the ature of Hierarchy Model represets the user iterest. The geeral process of creatig user iterest is like Fig.2. Iterest iformatio Iterest iformatio VSM Date process Clusterig Aalysis Behavior Aalysis Iterest Adjust Short-term iterest Log-term iterest Fig. 2: Traditioal process of creatig user iterest model 4. Summary ad commets 565

5 Explicit buildig of user iterestig model has several drawbacks. The user provides icosistet or icorrect iformatio, the model created is static whereas the user's iterests may chage over time, ad the processig of creatig user iterest model may waste user's time ad place a burde o the user [9], [10], [11].But i our view that we ca complete the iitializatio of user iterest model by the value that obtaied explicitly ad chage with the drift of user s iterest. The vector space model has some limitatios: Log documets are poorly represeted because they have poor similarity values. Ca ot capture the items i the documet fully for user s complex preferece. The order i which the terms appear i the documet ca t be represeted i the vector space. Not cosidered the syoym ad polysemy of the words. We suggested iterest model should be expressed as a tree structure has 2 layers, the first layer describes the categories of user s iterests ad the secod layer represets user s prefereces. User ID used to distiguish differet users. User ID Ad Iterest Category P P P feature items feature items feature items feature items feature items feature items Fig. 3: Iterest Tree Each user has bee created a tree structure to store iterests. The ode i the first layer has property amed p which is used to describe the frequecy of user s iterest i a certai period. I a period of time, the frequecy of usig the iterest reaches a certai threshold ad the the iterest should be attributed to be the log-term iterest. A log-term iterest have ot bee used i a log time should be forgotte. Nodes i the last layer store the feature items of user s iterests.t j is the descriptio of feature items, W j expressed the weight which gai by a certai method to the correspodig feature items. The process of creatig a user model is ofte eglected to collect user iformatio explicitly, ad ot cosider the order of browsig pages. Ad ot selected importat historical browsig iformatio. Therefore, we propose a method to create user iterest model. Step 1 Collectig ad aalyzig iformatio from the set of user s behavior, ad the selectig the valuable pages based o search paths. By filterig effective webs, we ca low the amout of memory required to store the details Step 2 Extractig feature words form the pages of Step1 ad the creatig VSM. Step 3 Calculatig the relevacy rakig of feature words ad search key words is i order to remove oise. Step 4 Iitial formatio of short-term iterest model Step 5 Adjustig to format the log-term iterest model. Decreasig the umber of web pages required, icreasig the umber of web pages efficacious iformatio cotaiig, improvig the accuracy of user iterest model, ad updatig iterest model as iterestig movig, are merits of the method metioed above. 566

6 User Behavior SET Log-term Iterest Short-term Iterest Date Process Similarity Calculatio Select Effective Iformatio Useful Iformatio Search Key Word Vector Space Clusterig Fig. 4: Steps of creatig user iterest model 5. Ackowledgmet The authors are thakful to the experts who check ad approve this paper, ad to the classmates i No.149 lab of School of Computer Sciece ad Techology i Doghua Uiversity. 6. Refereces [1] M. Claypool, P. Le, M. Wased ad D. Brow, Implicit iterest idicators I Proc of the IUI 01 New York: ACM Press, 2001: [2] J. Atteberg, S. Padey ad T. Suel, Modelig ad Predictig User Behavior I Proc of the KDD 09 New York: ACM Press, 2009: [3] E. Agichtei, E. Brill ad S. Dumais, Improvig web search rakig by icorporatig user behavior iformatio I Proc of the SIGIR 06 New York: ACM Press, 2006: [4] S. Fox, K. Karawat, M. Mydlad, S. Dumals ad T. White, Evaluatig implicit measures to improve web search ACM Tras. If. Syst New York: ACM Press, 2005: [5] R.W. White ad J. Huag, Accessig the sceic route: measurig the value of search trails i web logs I Proc of the SIGIR 10 New York: ACM Press, 2010: [6] M. Bileko ad R.W. White, Miig the search trails of surfig crowds:idetifyig relevat websites from user activity I Proc if the WWW2008 Beijig: 2008:51-60 [7] Y.H.Wu ad Y.C. Chi, Eablig persoalized recommedatio o the web based o user iterests ad behaviors I Proc of the RIDE Heidelberg: IEEE Press, 2001: [8] G.G. Chowdhury, Itroductio to Moder Iformatio Retrieval Lodo Facet Press, 2004: [9] M. Speretta ad S. Gauch, Persoalized Search Based o User Search Histories I Proc of the WI 05 Los Alamitos: IEEE Press, 2005: [10] D. Billsus ad M.J. Pazzai, A hybrid user model for ews story classificatio I Proc of the UM 99 Baff: ACM Press,1999: [11] C.C Che, M.C Che ad Y. Su A self-adaptive persoal view aget I Proc of the KDD 01 Sa Fracisco:ACM Press, 2001: