Information Filtering Using the Dynamics of the User Profile

Transcription

1 Informaton Usng the Dynamcs of the User Profle Costn Barbu, Marn Smna Electrcal Engneerng and Computer Scence Department Tulane Unversty New Orleans, LA, {barbu, Abstract Ths paper presents an adaptve algorthm for learnng the user profle. The user profle s learned ncrementally and contnuously based on user s ntal profle, hs actons and on semantc nterpretaton of queres usng hypernyms extracted by WordNet. A novel model, tme - words vector hyperspace, s ntroduced n order to keep track of the user s nterests changes. Ths new model s acheved by addng a temporal dmenson to the classcal vector hyperspace model. The results of the retreval experments usng ths new algorthm show an mproved effectveness over the current nformaton retreval technques. Keywords. User profle, nformaton flterng, nformaton retreval 1. Introducton In ths paper we nvestgate the role of the user profle n nformaton flterng and we ntroduce a novel algorthm for learnng the user profle. Informaton search on the WWW may become a frustratng actvty when a search engne returns thousands of documents for a gven query. One way to prune rrelevant documents s to take advantage of the user s mplct nterests to flter the documents returned by the search engne, or to reformulate the query based on these nterests. We can keep track of the user s nterests by buldng an ndvdual user profle and evolvng t over tme. The ssue s to dentfy what parts (areas of nterest) of the user profle are relevant n the current search context. In ths work we propose an adaptve algorthm for learnng the changes n user nterests. The user profle s learned ncrementally and contnuously based on hs ntal profle, hs actons and on semantc nterpretaton of queres usng hypernyms extracted by WordNet 1. In nformaton retreval, one of the common representatons of the documents (and queres) s based on vector hyperspace Copyrght 2003, Amercan Assocaton for Artfcal Intellgence ( All rghts reserved. 1 WordNet s an onlne lexcal reference system avalable at: model (Salton & McGll 1993). We extend the model for the purpose of nformaton flterng by takng nto account the user current nterests and ther decay n tme (f nterests change). The resultng model, tme - words vector hyperspace, computes the dynamcs of the user profle. Each dmenson of the vector space, but one (the temporal dmenson), represents a word and ts weght calculated usng the classcal TF-IDF technque (Salton & McGll 1993). In ths space the documents are represented as vectors, havng the word-components computed usng TF-IDF and the temporal dmenson set to zero. Queres are represented as feature vectors but n addton to the TF- IDF weghts they have the temporal dmenson set to a preset postve ntal value that decays n tme. The rest of the paper s organzed as follows. Secton 2 presents related work and ts lmtatons. Secton 3 ntroduces the user profle learnng algorthm. Secton 4 dscusses expermental results and fnally conclusons and future work are approached n the last secton. 2. Related Work Prevous work nvestgated varous approaches to learn the user s nterests. WebMate s an ntellgent agent that keeps track of the user nterests whle he s surfng the Internet (Chen & Sycara 1998). The user's profle s learned through multple TF-IDF feature vectors. Categores of nterests are learned based on the user s postve feedback. As long as the number of domans s below ts upper lmt, a new doman category s learned for every feedback. When the maxmum lmt has been reached, the document to be learned wll be employed to change the vector wth the greatest smlarty. WebMate ntroduces a trgger-par model to refne the document search. INFOS s a system that learns automatcally by adaptng ts user model (Mock 1996). The user nterests n dfferent domans are represented by feature vectors. Keywordbased and knowledge-based technques are employed for feature vector manpulaton. The accuracy over keyword approach s mproved by the hybrd approach. It also supports doman knowledge and retans the system s scalablty. FLAIRS

2 Balabanovc (1997) proposes an adaptve agent for Web browsng. The user profle s represented by a sngle feature vector weghted usng the TF-IDF technque. The vector weght s ncreased or decreased based on the explct postve or negatve user s feedback. Neural network technques have been used to learn user's profle n papers of many authors such as: Wener et al (1995), McEllgot & Sorensen (1994) or Tan & Teo (1998). Other authors explored genetc algorthms to learn user nterests by ncremental relevance feedback n NewT (Sheth 1993), and Amalthea (Moukas, A. & Zachara G. 1997). Wdyantoro (1999) developed Alpes, an ntellgent agent that learns user's nterests and provdes personalzed news artcles retreved from the Internet. Although most of the mentoned works deal wth learnng user's profle, they do not emphasze on the adaptaton of ther systems to the changng of the user nterests, except for the work of Wdyantoro. Nevertheless the dynamcs and the rate of change of the user nterests were not addressed n prevous work. These problems have been addressed by our adaptve algorthm for learnng the changes n user nterests based on hs ntal profle, hs actons, queres semantc nterpretaton and on a novel concept for trackng and analyzng dynamcs of the user profle: tme - words vector hyperspace. 3. Modelng and Learnng the User Profle Dynamcs Contextual relevant nformaton, ncludng user profle, has a crtcal role n nformaton flterng. In ths secton we shall ntroduce a new algorthm for dynamc learnng of user nterests based on hs ntal profle, hs actons and on queres semantc analyss. Each dmenson of the tme words vector hyperspace, but one (the temporal dmenson), represents a word. Its weght s calculated usng one of the classcal technques: Term Frequency Inverse Document Frequency (TF-IDF). As mentoned, the documents are represented as vectors wth word-components computed usng the TF-IDF technque, but the temporal dmenson s set to zero. In ths space, queres are represented as TF-IDF feature vectors wth an addtonal temporal dmenson (current nterest weght) set to a preset postve ntal value that decays n tme. Ths fact mples that some specfc user nterests could decrease as tme goes on. However, user nterest for a category can be mantaned/ncreased f the user s searchng for elements belongng to an already exstng category n hs profle. We have modeled the user behavor by developng an adaptve algorthm for dynamc learnng of the user profle based on mplct-only user s feedback. Ths algorthm uses WordNet to enhance the semantc analyss of the queres whenever ths s possble. WordNet s an onlne lexcal reference system developed by the Cogntve Scence Laboratory at Prnceton Unversty. Its desgn s nspred by current psycholngustc theores of human lexcal memory. Englsh nouns, verbs, adjectves and adverbs are organzed nto synonym sets, each representng one underlyng lexcal concept. Dfferent relatons lnk the synonym sets. WordNet also provdes varous senses for a gven word and ther correspondng hypernyms. A complete sequence of hypernyms startng from one of the senses of a word has been defned n ths paper as a hypernym chan. The scheme proposed n ths work keeps track of both the user s Recent and Long-Term Profles. The nput of the algorthm s an explct or mplct query and the output s one or more trplets (Category C, Current Interest Weght W, Rate of Interest Change α ). The user s Long-Term and Recent Profles are represented by two queues wth smlar structures, but the Long-Term Profle queue has a larger capacty than the Recent Profle queue. The recent nterest categores are added at the rear of the Recent Profle queue (as shown n the example from Fgure 1) and stored n the queue as long as the Current Interest Weght W s postve. As W becomes negatve, the correspondng trplet (C, W, α ) s moved to the rear of the Long-Term Profle queue. The same acton takes place f the Recent Profle queue reaches ts capacty. When the Long-Term Profle queue s at ts capacty, the trplet (C, W, α ) from the front of the queue s deleted. We consder that the Current Interest Weght decays lnearly wthn the Recent Profle perod of tme and exponentally n the Long-Term Profle tme nterval. The Rate of Interest Change (α ) s computed usng the cosne smlarty between two sequental query feature vectors Q and Q -1 as follows Q Q 1 α = (1) Q Q 1 Recent Profle musc show sport food Fgure 1. User Recent Profle representaton Categores Current Interest Weght Rate of Interest Change The algorthm ntroduced n ths paper s modelng the user behavor durng hs nformaton search actvty. The user can ether nput an explct query (when he types a set of keywords) or he can narrow hs search process when he clcks the lnks on the dsplayed web page; then he can scroll down durng the readng process n case the web page s of nterest for hm or he clcks on a dfferent lnk. In the latter stuaton an mplct query could be nferred based on the user s actons. In case the user does not fnd what he s lookng for, he can type another query and the search process goes on. Therefore two algorthms have been developed, for explct or mplct queres. 246 FLAIRS 2003

3 3.1 Learnng User Profle from Explct Queres We assume that the user has provded a prelmnary profle, hs Long Term Profle has at most L domans of nterest and hs Recent Profle has R domans of nterest. Assume the preset number of elements of a vector s M. The algorthm (LearnUserProfleExplct) for learnng the user profle from explct queres s defned as follows. Input: explct query EQ Output: updated user profle P LearnUserProfleExplct(EQ ) => profle P 1. For each query EQ = {t 1, t 2, t 3,, t k }, where k = 1 M and t k are the keywords of query EQ 2. Compute the Rate of Interest Change α between EQ and EQ For each keyword t k 4. Extract the hypernym chans HC k for all senses of t k from WordNet. 5. Do the ntersecton of the hypernym chans from step 4 wth each of the categores hypernym chans from the Recent Profle. If ths ntersecton s not vod then contnue wth step 6. Else contnue wth step Select the sense whose hypernym chan HC k ntersected the Recent Profle categores hypernym chans closest to the keyword s sense, and consder the HC k correspondng to the selected sense. 7. Do N = I HC k for all keywords t k of query EQ k 8. Extract θ from HC k (where θ s a threshold set of words,.e. the root and the next level chld (hyponym) from the tree lexcal structure of WordNet) 9. If sze(n ) > sze(θ) then 9.1 Extract the closest word from N to a keyword t k 9.2 Insert t to the Recent Profle as a Category C, together wth the Current Interest Weght W (preset to a postve ntal value W) and wth the Rate of Interest Change α 9.3 If Rate of Interest Change α > α threshold (say α threshold s 0.6) then ncrease the Current Interest Weght of Category C wth a postve value W : W = W + W 10. Else For all keywords t k of query EQ Do T = HCk I C j, where C j are exstng categores from Recent Profle and HC k have been selected at step If T s vod then add t k to the Recent Profle as a new Category C, together wth the Current Interest Weght W (preset to a postve ntal value W) and wth the Rate of Interest Change α Else ncrease the Current Interest Weght of C j to the preset postve ntal value W. 11. Sort the trplets (C, W, α ) from the Recent Profle n ascendng order of the Current Interest Weght W. 12. Return Updated User Profle. Note that steps 3 through 6 have been consdered n order to better dscrmnate among possble polysemantc keywords t k of query EQ takng nto account the contextual search envronment. 3.2 Learnng User Profle from Implct Queres We shall ntroduce the algorthm for learnng the user profle from mplct queres, as followng. Input: user actons Output: updated user profle P LearnUserProfleImplct(IQ ) => profle P 1. For each lnk mouse clck do: 2. Preprocess: parse HTML page, deletng the stop words, stemmng the plural noun to ts sngle form and nflexed verb to ts orgnal form. 3. Extract the words n ttle as a vector V T, and the words n the secton ttles as a vector V ST 4. Extract the vector V D for ths document usng the TF-IDF technque. 5. Compute the mplct query feature vector IQ IQ = w1 VT + w2 VST + w3 V where w 1, w 2, w 3 are weghts set to ntal values such that w 1 > w 2 > w 3 6. Update IQ accordng to user s behavor: f the user scroll down the document for a perod of tme shorter than an average readng tme then w 3 could be ncreased such that the above nequalty holds, snce the user has some nterest about ts content. On the other hand f the user scroll down the document for a perod of tme longer or equal than an average readng tme then he has a real nterest on the document and w 3 should be assgned wth a greater value than n prevous cases; hence w 3 > w 1 > w 2 7. Call LearnUserProfleExplct(IQ ) to learn the profle from mplct query IQ. 8. Return Updated User Profle. 4. Expermental Results and Dscusson 4.1 Experments for Learnng User Profle from Explct Queres Example 1 Let s assume the user s typng the Query 1 and hs recent profle has the followng categores of nterests: Recent Profle = {vehcle, art, sausage, pastry}; Query 1= {Shrmp, Chardonnay, Onon, Dressng}; WordNet provdes varous senses for each keyword: 3 senses of Shrmp : - small person - seafood - decapod crustacean 2 senses of Chardonnay : - vnfera grape - whte wne 3 senses of Onon : - bulb - allaceous plant - vegetable D FLAIRS

4 7 senses of Dressng : - sauce - concocton, mxture - enrchment - cloth coverng - converson - coverng - medcal care, medcal ad A hypernym chan example returned by WordNet for sense 3 of keyword onon s shown n Fgure 2. Sense 3 of word Onon onon => vegetable, vegge => produce, green goods, green groceres => food => sold => substance, matter => entty, physcal thng Fgure 2. Hypernym chan for sense 3 of keyword onon as provded by WordNet The followng results are acheved by applyng the algorthm for learnng the user profle from explct queres. Accordng to the steps 3, 4, 5, 6 of the algorthm, the hypernym chans for all the senses of keywords from query have been ntersected wth the hypernym chans of the categores from the Recent Profle and the followng keywords hypernym chans have been selected: Sense 2 of word Shrmp prawn,shrmp => seafood => food => sold => substance, matter => entty, physcal thng Sense 2 of word Chardonnay Chardonnay, Pnot Chardonnay => whte wne => wne, vno => alcohol, alcoholc beverage, ntoxcant, nebrant => beverage, drnk, drnkable, potable => food, nutrent => substance, matter => entty, physcal thng Sense 3 of word Onon onon => vegetable, vegge => produce, green goods, garden truck => food => sold => substance, matter => entty, physcal thng Sense 2 of word Dressng stuffng, dressng => concocton, mxture, ntermxture => foodstuff, food product => food, nutrent => substance, matter => entty, physcal thng The category Food s extracted from Query 1 and added to the Recent Profle accordng to the steps 7, 8 and 9 of the algorthm. Example 2 In ths example the user has a dfferent profle and he nputs Query 2. Recent Profle = {vehcle, art, sausage, pastry, food} Query 2 = {Skatng, Mathematcs, Anecdote}; WordNet outputs only one sense for each of the keywords of Query 2 and the followng hypernym chans: Sense 1 of Skatng skatng => sport, athletcs => dverson, recreaton => actvty => act, human acton, human actvty Sense 1 of Mathematcs mathematcs, math, maths => scence, scentfc dscplne => dscplne, subject, subject area, subject feld, feld, feld of study, study, balwck, branch of knowledge => knowledge doman, knowledge base => content, cogntve content, mental object => cognton, knowledge, noess => psychologcal feature Sense 1 of Anecdote anecdote => report, account => nformng, makng known => speech act => act, human acton, human actvty By applyng the algorthm for learnng the user profle from explct queres the hypernym chans for all the senses of keywords from query have been ntersected wth the hypernym chans of the categores from the Recent Profle. Although no ntersecton has been found wth the exstng categores hypernym chans, the next step we take s dong the ntersecton of the hypernym chans of the keywords from query, accordng to the steps 7 of the algorthm. Snce sze(n ) < sze(θ) at step 9, we jump to step 10 and compute the set T. After all these steps, T has been found to be vod and all the keywords from Query 2 should be added to the Recent Profle as new categores, accordng to step 10.3 of the algorthm. Recent Profle = {vehcle, art, sausage, pastry, food, skatng, mathematcs, anecdote} 4.2. Informaton Based on User Profle Contextual relevant nformaton mproves the search performance by flterng the retreved documents n descendng order of the Relevance Score. Ths Relevance Score can be computed as the cosne smlarty between the User Recent Profle feature vector and the feature vectors of the documents retreved by a classcal search engne (.e. Google). Our prelmnary results show that the qualty of nformaton flterng based on user recent profle s dramatcally mproved by takng nto account the rate of user s nterest change and the polysemantc dsambguaton of the query s keywords. More documents relevant to the current nterest of the user are retreved. Sample results are presented n the Table Dscusson Another approach of the algorthm for learnng the user profle from explct queres (LearnUserProfleExplct) could be the followng. Assume an explct query EQ has been nput by the user, EQ = {t 1, t 2, t 3,, t k }, where k = 1 M and t k are the keywords of the query. Let s consder that steps 1 thru 7 of the algorthm LearnUserProfleExplct have been already performed and a clusterng threshold has been set at 30%. 248 FLAIRS 2003

5 Total Number Documents Retreved Relevant Documents n Top 10 Retreved Accuracy n Top 10 Documents Retreved No Profle Total Profle Recent Profle % 20 % 100 % Table 1. Comparson of nformaton flterng methods If say at least 70 % of the keywords have hypernym chans that ntersect each other and form clusters around N dfferent categores, then the rest of the keywords (less than 30 %) from EQ that do not belong to any of the N clusters could be consdered nose and be gnored. An example of ths stuaton s presented n Fgure 3, where the explct query has 5 keywords. Two categores are extracted from ths query (seafood and musc) and added to the user s Recent Profle whereas the keyword plane s consdered nose. shrmp seafood crab blues musc jazz Fgure 3. Keywords clusterng representaton 5. Conclusons Categores plane EQ In ths paper we presented an adaptve algorthm for learnng the changes n user nterests based on hs ntal profle, hs actons and on semantc nterpretaton of queres. We ntroduced a novel concept, Tme - Words Vector Hyperspace to computatonally model the rate of nterest change and the dynamcs of the user profle. We also added adaptve polysemantc dsambguaton of the user s query usng WordNet. Snce our algorthm does not rely on semantc dsambguaton for short queres, we avod the performance degradatons mentoned by Voorhees (1993). Our prelmnary results show a sgnfcant mprovement of flterng by employng the user recent profle as opposed to exstng approaches (e.g. Chen & Sycara 1998, Balabanovc 1997, Wdyantoro et al. 1999) that consder the total profle. Our mplementaton currently does not handle queres wth brand name keywords (.e.: Sun, computer maker vs. sun, star) snce WordNet does not nclude them. We can overcome ths stuaton n our future work by buldng and ntegratng wth WordNet a specalzed ontology that ncludes brand names. References Balabanovc, M An Adaptve Web Page Recommendaton Servce. In Proceedngs of the Frst Internatonal Conference on Autonomous Agents , New York. N.Y.: ACM Chen, L., and Sycara, K WebMate: Personal Agent for Browsng and Searchng. In Proceedngs of the Second Internatonal Conference on Autonomous Agents, New York. N.Y.: ACM McEllgot, M. and Sorensen, H An Evolutonary Connectonst Approach to Personal Informaton. In Proceedngs of the Fourth Irsh Neural Network Conference, , Dubln, Ireland. Mock, K. J Hybrd-Hll-Clmbng and Knowledge-based Technques for Intellgent News. In Proceedngs of the Thrteenth Natonal Conference on Artfcal Intellgence and the Eghth Innovatve Applcatons of Artfcal Intellgence Conference Menlo Park, Calforna: AAAI Press Moukas, A. and Zachara G Evolvng a Multagent Informaton Soluton n Amalthea. In Proceedngs of the Frst Internatonal Conference on Autonomous Agents, New York, N.Y.: ACM Salton, G., and McGll, M. J Introducton to Modern Informaton Retreval. New York. N. Y.: McGraw-Hll. Sheth, B. D A Learnng Approach to Personalzed Informaton. M.S. dss., Dept. of Electrcal Engneerng and Computer Scence, Massachusetts Insttute of Technology. Tan, A. and Teo, C Learnng User Profle for Personalzed Informaton Dssemnaton. In Proceedngs of 1998 Internatonal Jont Conference on Neural Networks, , Anchorage, AK: IEEE. Voorhees, E.M Usng WordNet to Dsambguate Word Senses for Text Retreval. In Proceedngs of the 16th Annual Internatonal ACM-SIGIR Conference on Research and Development n Informaton Retreval, , Pttsburgh, PA Wdyantoro, D. H., Yn J., El Nasr, M., S., Yang, L., Zacch, A. and Yen J Alpes: A Swft Messenger n Cyberspace. In Proceedngs of the Sprng Symposum on Intellgent Agents n Cyberspace, 62-67, Palo Alto, CA. Wener, E., Pederson, J. and Wegend, A A Neural Network Approach to Topc Spottng. In Proceedngs of the Fourth Annual Symposum on Document Analyss and Informaton Retreval, , Las Vegas, NV. FLAIRS