Recommendations of Personal Web Pages Based on User Navigational Patterns

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1 nternatonal Journal of Machne Learnng and Computng, Vol. 4, No. 4, August 2014 Recommendatons of Personal Web Pages Based on User Navgatonal Patterns Yn-Fu Huang and Ja-ang Jhang Abstract n ths paper, we propose a web recommendaton system where user navgatonal patterns can be extracted from web logs. Frst, the recommendaton system dscovers user concepts from web logs step-by-step, and then extracts the navgaton patterns among these concepts. hese navgatonal patterns are then used to generate recommendaton web pages by matchng the navgaton behavor of a user personal knowledge base. he pages n a recommendaton lst are ranked accordng to ther hub scores whch are computed based on page connectvty nformaton. he expermental results show that the web pages recommended by our system are of better qualty and acceptable for humans from varous domans, based on human evaluators rankng as well as qualty-value-based performance measures. ndex erms nformaton retreval, recommendaton system, web mnng, web search.. NRODUCON As more and more nformaton s avalable on the nternet, web search has become an essental tool for users. However, sometmes a user query s ambguous and cannot clearly descrbe what he/she wants. For example, f the keyword specfed n a user query s apple, t cannot ndcate frut or computer scence explctly. n ths paper, we propose a soluton to ths problem by constructng a web recommendaton system whch ntegrates personal knowledge bases over dfferent domans to help users to extract desred nformaton. A web recommendaton system s an onlne nformaton system that recommends relevant tems to users. he recommended tems could be products, moves, or even on-lne resources such as web pages. ypcally, a web recommendaton system s composed of an off-lne module and on-lne module. he off-lne module dscovers user navgaton patterns from web logs, whle the on-lne module matches the navgaton behavor of a current user wth dscovered navgaton patterns to produce a recommendaton lst. n recent years, many recommendaton systems have been developed to facltate web search. However, the common problem wth these recommendaton systems s that they generally requre a great amount of documents collected from the vsted webstes by users and the nformaton from user. BASC CONCEPS Some technques and approaches are used n our work. Here, we would brefly revew them n the followng subsectons. A. Query Classfcaton he classfcaton/categorzaton of web queres s usually an nvestgated ssue n computer scence. he task s to assgn a web query to one or more predefned categores, based on ts topcs. he mportance of query classfcaton was emphaszed by many servces provded by web search. A drect applcaton s to provde better web pages for users wth mult-category nterests. For example, a user ssung a web query apple mght expect to browse the web pages related to frut apple, or he/she may prefer to look over the products or news related to the company Apple. Onlne advertsement servces rely on query classfcaton results to promote products more accurately. Web pages can be grouped together accordng to the categores predcted by a query classfcaton algorthm. However, the computaton of query classfcaton s non-trval. Dfferent from document classfcaton tasks, the queres submtted by web users are usually short and ambguous, and ther meanngs are evolvng over tme. herefore, query classfcaton s much more dffcult than tradtonal document classfcaton tasks [2]-[4]. Manuscrpt receved February 13, 2014; revsed May 12, hs work was supported by Natonal Scence Councl of R.O.C under Grant NSC E MY3. Yn-Fu Huang s wth Natonal Yunln Unversty of Scence and echnology, Yunln, awan 640 (e-mal: huangyf@yuntech.edu.tw). Ja-ang Jhang s wth ornado echnologes Co., Ltd, ape, awan 106 (e-mal: @yuntech.edu.tw). DO: /JMLC.2014.V4.429 nteractons. n ths paper, we propose a novel approach nstead of collectng a huge amount of data. n general, a user sesson has more than one query to be fulflled [1]. hus, the queres of a user sesson n web logs can be clustered based on the probabltes n dfferent domans, and these clusters are called concepts. n other words, a concept s a sesson wth coherent nformaton, whch s constructed dynamcally and not pre-defned. hen, these concepts are augmented wth ther connected neghborhoods and fnally generate navgaton patterns. Next, the navgaton patterns already captured are compared wth the semantc graph n a personal knowledge base. Fnally, we recommend the most relevant web pages n the matched clusters. he remander of the paper s organzed as follows. Frst, related basc concepts and personal knowledge bases are ntroduced n Secton. hen, Secton descrbes each component n the system archtecture. Secton V dscusses the expermental results. Fnally, we make conclusons n Secton V. B. Fuzzy Clusterng Clusterng s the task of assgnng a set of obects nto groups (called clusters) so that the obects n the same cluster 307

2 nternatonal Journal of Machne Learnng and Computng, Vol. 4, No. 4, August 2014 are more smlar (n some sense or another) to each other than to those n other clusters. ypcally, clusterng technques come n two notons: hard and soft. n hard clusterng [5], data s dvded nto dstnct clusters, where each data element belongs to exactly one cluster. n soft clusterng (or fuzzy clusterng) [6], each data element has a certan probablty of belongng to each of the clusters, as shown n Fg. 1. One can thnk of hard clusterng as a specal case of soft clusterng where these probabltes only take values 0 or 1. C. Lnk Analyss Algorthm Fg. 1. Fuzzy clusterng. he analyss of hyperlnks has been nstrumental n the development of web search. here are several algorthms of the lnk analyss; e.g., the PageRank algorthm and HS (.e., Hypertext nduced opc Selecton) algorthm are two popular algorthms. PageRank consders the hyperlnk weght normalzaton and the balance dstrbuton of random surfers as the ctaton score [7]. HS makes the dfferentaton between hubs and authortes, and estmates them n a mutually renforcng way. D. Personal Knowledge Base A personal knowledge base (PKB) s knowledge repostory used to store the personal knowledge of an ndvdual. A PKB dffers from a tradtonal database where t contans subectve materal partcular to the owner. mportantly, a PKB conssts prmarly of knowledge, rather than nformaton; n other words, t s not a collecton of documents or other sources that an ndvdual has encountered, but rather an expresson of the dstlled knowledge that the owner has extracted from those sources [8]. A. Overvew. SYSEM ARCHECURE he archtecture of the web recommendaton system, as shown n Fg. 2, conssts of fve components: 1) preprocessor, 2) query classfcaton, 3) concept dentfcaton, 4) HS algorthm, and 5) recommendaton engne. Snce dfferent users have ther own nterests n dfferent domans such as scence, art, sports et al., the recommendaton system dscovers user concepts from web logs step-by-step, and then extracts the navgaton patterns among these concepts. Afterwards, the recommendaton engne dentfes the navgaton behavor of a current user by hs/her personal knowledge base [9] and matches the behavor wth the dscovered navgaton patterns. f navgaton patterns are found, the engne would recommend a lst of web-pages for hs/her browsng. B. Preprocessor Frst, the preprocessor parses or splts up web logs nto three fles: 1) query fle, 2) user vstng fle, and 3) page connectvty nformaton. All of these fle records nformaton regardng users request to web logs. he query fle collects all of queres recorded n web logs. he user fle ncludes anonymous user Ds, queres ssued by users, and URLs clcked on search results. he page connectvty nformaton records the connectvty of these URLs n web logs. C. Query Classfcaton ypcally, the queres ssued by users consst of a few words. Snce these queres are usually very short and ambguous, how to nterpret the queres n terms of multple domans s the maor problem of concept dentfcaton. Here, we use the taxonomy-brdgng algorthm [3] to solve ths problem, whch classfes a user query Q k nto a set of n categores {C 1, C 2,, C n }. As a result, we can represent a query as a vector Q k = (p(c k1 ), p(c k2 ),, p(c kn )) where p(c k ) s the probablty of query Q k belongng to category C. An example of the target categores s llustrated n Fg. 3. Because no data are provded to defne the contents and semantcs of a category and query, a straghtforward method s to submt them to a search engne for extractng related pages. he extracted pages can help determne the meanngs of the categores and queres. n [3], Shen et al. connect the target categores and queres by takng ntermedate categores as a brdge. As llustrated n Fg. 4, the squares n the left part denote the queres to be classfed; the tree n the rght part represents a herarchy organzed by the target categores; the tree n the mddle part s an exstng ntermedate taxonomy used n the Open Drectory Proect (ODP) [10]. he thckness of the dotted lnes reflects the smlarly relatonshp between two nodes. For example, gven a target category C and a query q k, we can udge the smlarty between them by the dstrbutons of ther relatonshp to the ntermedate category C and C. For the followng formula used n the taxonomy-brdgng algorthm, p( C q) s the condtonal probablty of a target category C, gven a query q. Smlarly, p( C C ) and p(q C ) are the condtonal probabltes of a target category C and a query q respectvely, gven an ntermedate category C. p( C ) s the pror probablty of a ntermedate category C, whch can be estmated from the web pages n the ntermedate categores C. f a target category C s represented by a set of words (w 1, w 2,, w n ) where each word w k appears n k tmes, p( as n k 1 n p( w C ) k k C C k ) can be calculated. Fnally, p(q C ) can be calculated n the same way as p( C C ). hus, the formula can be 308

3 nternatonal Journal of Machne Learnng and Computng, Vol. 4, No. 4, August 2014 Fg. 2. System archtecture. expressed as follows: represented by a set of queres {Amercan League, Boston Red Sox, Oakland Athletcs,, Maor League Baseball}. p(c q) C p(c C ) where p(q C ) p(c ) p(q) 1) Query Clusterng As descrbed n Secton, a query s regarded as a vector. hen, all queres of a user sesson can be clustered based on ther semantcs from vectors. A concept models queres related to one of user ntentons, and a concept can overlap other concepts snce a query s usually ambguous. One of the popular fuzzy clusterng s the Fuzzy C Means (FCM) algorthm whch allows each query to belong to more than one cluster. hs method was developed by Dunn n 1973 [11] and mproved by Bezdek n 1981 [12], and t s frequently used n pattern recognton. he FCM algorthm s based on mnmzng the followng obectve functon: (1) p(c C ) k 1 p(wk C )nk n Fg. 3. Example of the target categores. N c J m um x c 2, 1 m (2) 1 1 where m s any real number greater than 1, N s the number of measured data, u s the degree of membershp of x n the cluster, x s the th d-dmensonal measured data, c s the d-dmenson center of the cluster, and * s the norm expressng the smlarty between the measured data and center. Fuzzy parttonng s carred out through an teratve optmzaton of the obectve functon shown above, wth the update of membershp u and the cluster center c by: Ck C qk C Fg. 4. llustraton of the taxonomy-brdgng. u D. Concept dentfcaton A web query log contans anonymous user Ds, queres ssued by users, clcked URLs, and other related nformaton. n general, sesson detecton s done by consderng the tme length of a user sesson, and a user sesson has more than one query to be fulflled [1]. Here, we propose a novel method where the queres of a user sesson are clustered based on the vectors of the probabltes n dfferent target categores, and we call these clusters concepts;.e., a concept s a sesson wth coherent nformaton, whch s constructed dynamcally and not pre-defned. For example, the concept baseball can be 1 x c k 1 k c x c N c u 1 N (3) 2 m 1 x (4) u 1 m he teraton wll stop when max u( k 1) u( k ), where ε s a termnaton crteron between 0 and 1, and k s the 309

4 nternatonal Journal of Machne Learnng and Computng, Vol. 4, No. 4, August 2014 teraton step. Fnally, ths procedure converges to a local mnmum or a saddle pont of Jm. n summary, the algorthm s composed of the followng steps: 1. ntalze U=[u] matrx;.e., U(0). 2. At kth-step: calculate the center vectors C(k)=[c] usng (k) U. 3. Update U(k) nto U(k+1). 4. f U(k+1) - U(k) <ε then SOP; otherwse return to step 2. Snce the FCM algorthm s very senstve to ntal cluster centers, we use the ant based algorthm [13] to obtan ntal cluster centers. keywords) to match the navgaton patterns prevously dscovered n the HS algorthm. For matchng patterns, we use the same query-based smlarty functon mentoned n Secton to udge ther relatonshp. f they are close enough (or the smlarty value s more than γ), the hgh-scorng (or top-ranked) URLs n matched concepts would be recommended to the current user. Here, the number of recommended URLs (or top-k) from each matched concept can be expressed as follows: k round w N 2) Concept Mergng After clusterng the queres, each concept s represented by a set of queres;.e., concept = {query1, query2,, queryn}. Snce the concepts may be smlar to each other, we use query-based smlarty to udge whether a par of concepts are close enough. f a concept has strong smlarty wth another concept, then two concepts wll be merged. he query-based smlarty functon s defned as follows: Smlarryquery (c1, c2 ) C (c1, c2 ) Max(l (c1 ), l (c2 )) where w s the normalzed smlarty rato of concept and N (also specfed by the current user) s the total number of recommended URLs. n other words, the matched concepts wth more smlarty values would contrbute more recommended URLs. V. EXPERMENS hs secton descrbes how to evaluate the web recommendaton system usng dscovered user concepts. Fve evaluators are nvted to udge the lst of pages recommended by our system;.e., assgnng a numerc score to each lst. he web recommendaton system s mplemented n Java, and the experments are conducted on an ntel Core GHz CPU wth 4G man memory n Wndow XP professonal. (5) l (c) s the number of queres n the concept c, C (c1, c2 ) s the number of common queres n two concepts, where and γ s a threshold to udge whether a par of concepts are close enough. A. Dataset n ths paper, we use the dataset from AOL (.e., Amercan Onlne), whch s avalable on the Web and ncludes more than 30 mllon (non-unque) web queres collected from more than 650,000 users over three months. hs dataset was sorted by user Ds and sequentally ordered. For each request, there s also nformaton about when the query was ssued, when a lnk was clcked, the ranks of lnks, and the URLs of lnks. E. HS Algorthm For a concept, each query s nvolved wth at least one URL so that we can measure these URLs by lnk analyss. n ths secton, we use the HS algorthm to measure these URLs [14] where each URL has both a hub score and an authorty score. A good hub page s one that ponts to many good authortes; a good authorty page s one that s ponted to by many good hub pages. More precsely, let h and a denote the vectors of all hub and all authorty scores, respectvely. Gven a concept wth a set of URLs, frst, the HS algorthm produces an n-by-n adacency matrx A whose element A s 1 f URL references to URL and 0 otherwse. hen, the HS algorthm terates the followng equatons: h Aa (6) a A h (7) (8) B. Performance Measures Here, we nvte fve evaluators maorng n computer scences to rate the web pages recommended by our web recommendaton system. he personal knowledge bases (n 10 specfc domans) of each evaluator have been bult from the web pages whch he/she selects through Google and Yahoo search engnes. hese domans are dverse, ncludng baseball, musc, computer networkng, mltary, dancng, gamblng, car, comc & anmaton, nvestng, and relgon & sprtualty. n order to observe the effectveness of our recommendaton system, we use the acceptable percentage measure defned by Zhang et al. [15] for ratng each page as a 1-to-5 scale (1: not related, 2: poorly related, 3: farly related, 4: well related, and 5: strongly related). Besdes, we also use a qualty value measure to evaluate the qualty of the pages recommended by our system as follows. he acceptable percentage measure: where A denotes the transpose of the matrx A. Snce the teratve updates capture the ntuton of good hubs and good authortes, the hgh-scorng URLs would be good hubs and authortes for the concept. Fnally, we can regard the concept wth the vectors of all hub and all authorty scores as navgaton patterns. F. Recommendaton Engne n the on-lne phase, we extract top-weghted key-phrases concernng a specfc category from the personal knowledge base of a current user [9] where the number of top-weghted key-phrases and the target category are specfed by the current user through GU. hen, we use these key-phrases (or m1 310 n3 n4 n5 5 n 1 (9)

Expermental Results and Dscussons n the experments, these fve testers evaluate the system by usng top-weghted key-phrases under dfferent op-n recommendatons (.e., op-5, op-10, and op-15).

Here, only the results of usng 10 top-weghted key-phrases under dfferent op-15 recommendatons are shown n Fg. 5 and Fg. 6.

5 nternatonal Journal of Machne Learnng and Computng, Vol. 4, No. 4, August 2014 he qualty value measure: n n 5 m2 1 5 (10) 1 where n1, n2, n3, n4, and n5 are the number of recommended web pages wth a score of 1, 2, 3, 4, and 5, respectvely. C. Expermental Results and Dscussons n the experments, these fve testers evaluate the system by usng top-weghted key-phrases under dfferent op-n recommendatons (.e., op-5, op-10, and op-15). Besdes, the threshold γ s emprcally set to 0.7 so that the system can udge whether a par of concepts are close enough. Here, only the results of usng 10 top-weghted key-phrases under dfferent op-15 recommendatons are shown n Fg. 5 and Fg. 6. We found that the acceptable percentages of most domans are more than 80% and the qualty values of most domans are more than 3.5. hs means that our system works well on varous domans. 7 and Fg. 8, we found that the average acceptable percentage s more than 83% and the average qualty value s more than 3.4. All fve evaluators are very satsfed wth ther own lsts of the recommended web pages. Besdes, we also compare our method wth the method only usng personal knowledge bases. he results ndcate that our method always performs better than the method only usng personal knowledge bases. hs verfes that the navgaton patterns extracted from the dscovered concepts defntely facltate recommendng web pages. Fg. 7. Average acceptable percentage for each evaluator. Fg. 5. Acceptable percentages of all domans for op-15. Fg. 8. Average qualty value for each evaluator. V. CONCLUSONS n ths paper, we propose a web recommendaton system where user navgatonal patterns can be dscovered from web logs. hese navgatonal patterns are then used to generate recommendaton web pages by matchng the navgaton behavor of a user personal knowledge base. he pages n a recommendaton lst are ranked accordng to ther hub scores whch are computed based on page connectvty nformaton. he expermental results show that the web pages recommended by our system are of better qualty and acceptable for humans from varous domans, based on human evaluators rankng as well as qualty-value-based performance measures. Fg. 6. Qualty values of all domans for op-15. For each evaluator evaluatng our method as shown n Fg. 311

nternatonal Journal of Machne Learnng and Computng, Vol. 4, No. 4, August 2014 REFERENCES [1] D. He and A. Goker, Detectng sesson boundares from web user logs, n Proc.

Freder, Automatc classfcaton of web queres usng very large unlabeled query logs, ACM ransactons on nformaton Systems, vol. 25, no. 2, artcle 9, 2007. [3] D. Shen, J.. Sun, Q. Yang, and Z.

6 nternatonal Journal of Machne Learnng and Computng, Vol. 4, No. 4, August 2014 REFERENCES [1] D. He and A. Goker, Detectng sesson boundares from web user logs, n Proc. the 22nd BCS-RSG Annual Colloquum on nformaton Retreval Research, 2000, pp [2] S. M. Betzel, E. C. Jensen, D. D. Lews, A. Chowdhury, and O. Freder, Automatc classfcaton of web queres usng very large unlabeled query logs, ACM ransactons on nformaton Systems, vol. 25, no. 2, artcle 9, [3] D. Shen, J.. Sun, Q. Yang, and Z. Chen, Buldng brdges for web query classfcaton, n Proc. the 29th Annual nternatonal ACM SGR Conference on Research and Development n nformaton Retreval, 2006, pp [4] J. R. Wen, J. Y. Ne, and H. J. Zhang, Query clusterng usng user logs, ACM ransactons on nformaton Systems, vol. 20, pp , [5] H. J. Zmmermann, Fuzzy Set heory and ts Applcatons, Kluwer Academc Publshers, [6] A. Baneree, S. Merugu,. S. Dhllon, and J. Ghosh, Clusterng wth Bregman dvergences, Journal of Machne Learnng Research, vol. 6, pp , [7] C. Dng, X. He, P. Husbands, H. Zha, and H. D. Smon, PageRank, HS and a unfed framework for lnk analyss, n Proc. the 25th Annual nternatonal ACM SGR Conference on Research and Development n nformaton Retreval, 2002, pp [8] S. Daves, Stll buldng the memex, Communcatons of the ACM, vol. 54, pp , [9] Y. F. Huang and C. S. Cou, Constructng personal knowledge base: automatc key-phrase extracton from multple-doman web pages, Lecture Notes n Computer Scence, vol. 7104, 2012, pp [10] he Open Drectory Proect. [Onlne]. Avalable: [11] J. C. Dunn, A fuzzy relatve of the SODAA process and ts use n detectng compact well-separated clusters, Journal of Cybernetcs, vol. 3, pp , [12] J. C. Bezdek, Pattern Recognton wth Fuzzy Obectve Functon Algorthms, Kluwer Academc Publshers Norwell, [13] P. M. Kanade and L. O. Hall, Fuzzy ants as a clusterng concept, n Proc. the 22th nternatonal Conference of North Amercan Fuzzy nformaton Processng Socety, 2003, pp [14] J. M. Klenberg, Authortatve sources n a hyperlnked envronment, Journal of ACM, vol. 46, pp , [15] Y. Zhang, E. Mlos, and N. Zncr-Heywood, Narratve text classfcaton for automatc key phrase extracton n web document corpora, n Proc. 7th Annual ACM nternatonal Workshop on Web nformaton and Data Management, 2005, pp Yn-Fu Huang receved the B.S. degree n computer scence from Natonal Chao-ung Unversty n 1979, and the M.S. and Ph.D. degrees n computer scence from Natonal sng-hua Unversty n 1984 and 1988, respectvely. He s currently a professor n the Department of Computer Scence and nformaton Engneerng, Natonal Yunln Unversty of Scence and echnology. Between July 1988 and July 1992, he was wth Chung Shan nsttute of Scence and echnology as an assstant researcher. Hs research nterests nclude database systems, multmeda systems, data mnng, moble computng, and bonformatcs. Ja-ang Jhang receved hs B.S. degree n computer scences from Natonal Unted Unversty and M.S. degree n computer scences from Natonal Yunln Unversty of Scence and echnology n 2010 and 2012, respectvely. He s currently servng n ornado echnologes Co., Ltd. Hs maor areas of nterests are database systems and data mnng. 312

Query Clustering Using a Hybrid Query Similarity Measure

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