A Recommender System for Online Personalization in the WUM Applications

Transcription

1 A Recommende System fo Online Pesonalization in the WUM Applications Mehdad Jalali 1, Nowati Mustapha 2, Ali Mamat 2, Md. Nasi B Sulaiman 2 Abstact foeseeing of use futue movements and intentions based on the uses clicksteam data is a main challenging poblem in Web based ecommendation systems. Web usage mining based on the uses clicksteam data has become the subject of exhaustive eseach, as its potential fo web based pesonalized sevices, pedicting use nea futue intentions, adaptive Web sites and custome pofiling is ecognized. A vaiety of the ecommende systems fo online pesonalization though web usage mining have been poposed. Howeve, the quality of the ecommendations in the cuent systems to pedict uses futue intentions systems cannot still satisfy uses specially fo long patten of use activities in paticula web sites. In this pape, to povide online pedicting effectively, we develop a model fo online pedicting though web usage mining system and popose a novel appoach fo classifying use navigation pattens to pedict uses futue intentions. The appoach is based on the using longest common subsequence (LCS) algoithm to classify cuent use activities to pedict use next movement. We have tested ou poposed model on the CTI datasets. The esults indicate that the appoach can impove the quality of the system fo the pedictions. Moeove, by using LCS, we can achieve moe accuate ecommendation fo long pattens of the cuent use activities in the paticula web sites. Index Tems Recommende Systems, Web Usage Mining, Longest Common Subsequence. I. INTRODUCTION Web based ecommende systems use the opinions of a community of uses to help individuals in that community moe effectively identify content of inteest fom a potentially ovewhelming set of choices [1]. Web-based ecommende systems have been shown to geatly help Web uses in navigating the Web, locating elevant and useful infomation, and eceiving dynamic ecommendations fom Web sites on possible poducts o sevices that match thei inteests. Web usage mining is one of the main appoaches used to build Web ecommende systems. Meanwhile, the substantial incease in the numbe of websites pesents a challenging task fo webmastes to oganize the contents of the websites to cate to the needs of uses. Modeling and analyzing web navigation behavio is helpful in undestanding what infomation of online uses demand. Following that, the analyzed esults can be seen as knowledge to be used in intelligent online 1 Mehdad Jalali is with depatment of softwae engineeing of Islamic Azad Univesity, Mashhad banch, Mashhad, Ian and he is a Ph.D. candidate of compute science in Univesiti Puta Malaysia, mehdadjalali@ieee.og. 2 Assistant Pof. D. Nowati Mustapha, Associate Pof. D. Md Nasi Sulaiman, and Associate Pof. D. Ali Mamat ae with Faculty of Compute Science and Infomation Technology, Univesiti Puta Malaysia, 434 UPM, Selango, Malaysia. s: {nowati,nasi,ali}@fsktm.upm.edu.my. applications, efining web site maps, web based pesonalization system and impoving seaching accuacy when seeking infomation. Nevetheless, an online navigation behavio gows each passing day, and thus extacting infomation intelligently fom it is a difficult issue. Web usage mining efes to the automatic discovey and analysis of pattens in clicksteam and associated data collected o geneated as a esult of use inteactions with Web esouces on one o moe Web sites [2-4]. Web usage mining has been used effectively as an appoach to automatic pesonalization and as a way to ovecome deficiencies of taditional appoaches such as collaboative filteing. The goal of pesonalization based on Web usage mining is to ecommend a set of objects to the cuent (active) use, possibly consisting of links, ads, text, poducts, and so foth, tailoed to the use s peceived pefeences as detemined by the matching usage pattens. This task is accomplished by matching the active use session with the usage pattens discoveed though Web usage mining ecommendation systems. In this pape, to povide online pedicting effectively, we develop a model fo online pedicting though web usage mining system and popose a novel appoach fo classifying use navigation pattens to pedict uses futue intentions. The appoach is based on the using longest common (LCS) subsequence algoithm to classify cuent use activities to pedict use next movement. The est of this pape is oganized as follows: In section 2, we eview ecent eseach advances in web usage mining. Section 3 descibes the system design and section 4 focuses on the use classifying based on the longest common subsequence. The system evaluation and the esults of expeimental evaluations ae epoted in sections 4 and 5. Finally, section 6 summaizes the pape and intoduces futue wok. II. RELATED WORK Web based ecommende systems ae vey helpful in diecting the uses to the taget pages in paticula web sites. Moeove, Web usage mining ecommende systems have been poposed to pedict use s intention and thei navigation behavios. In the following, we eview some of the most significant WUM ecommende systems and achitectue that can be compaed with ou system. Jalali et al. [5, 6] poposed a ecommende system fo navigation patten mining though Web usage mining to pedict use futue movements. The appoach is based on the gaph patitioning clusteing algoithm to model use navigation pattens fo the navigation pattens mining phase. Futhemoe, in the ecommende phase, longest common

2 subsequence algoithm is utilized to classify cuent use activities to foesee use next movement. Mobashe et al., pesent WebPesonalize a system which povides dynamic ecommendations, as a list of hypetext links, to uses [7, 8]. The analysis is based on anonymous usage data combined with the stuctue fomed by the hypelinks of the site. Data mining techniques (i.e. clusteing, association ules and sequential patten discovey) ae used in the pepocessing phase in ode to obtain aggegate usage pofiles. In this phase Web seve logs ae conveted in clustes made up of sequences of visited pages, and cluste made up of set of pages with common usage chaacteistics. The online phase consides the active use session in ode to find matches among the use s activities and the discoveed usage pofiles. Matching enties ae then used to compute a set of ecommendations which will be inseted into the last equested page as a list of hypetext links. WebPesonalize is a good example of two-tie achitectue fo Pesonalization systems. Baaglia and Palmeini poposed a WUM system called SUGGEST, that povide useful infomation to make easie the web use navigation and to optimize the web seve pefomance [9, ]. SUGGEST adopts a two levels achitectue composed by an offline ceation of histoical knowledge and an online engine that undestands use s behavio. As the equests aive at this system module it incementally updates a gaph epesentation of the Web site based on the active use sessions and classifies the active session using a gaph patitioning algoithm. Potential limitation of this achitectue might be: a) the memoy equied to stoe Web seve pages is quadatic in the numbe of pages. This might be a sevee limitation in lage sites made up of millions of pages; b) it does not pemit us to manage Web sites made up of pages dynamically geneated. All of these woks attempt to find achitectue and algoithm to impove quality of the pesonalized ecommendation, but the ecommendations still do not meet satisfaction. In ou wok, we advance a model and popose novel appoach to pedict use intention in the nea futue equest. III. SYSTEM DESIGN In this pape, a model is poposed to pedict use s futue equests and intentions. Geneally, Web Usage Mining ecommende systems consist of two main phase. Mining of use navigation pattens has done in offline phase of the ecommende system. In the online phase, the ecommende system pedicts a set of web pages as use next intentions. We have done navigation pattens mining based on the gaph patitioning algoithm as offline phase of a ecommende system [11]. Figue 1 illustates the model of the system. Accoding to diffeent pat of the poposed model, online components in a majoity of pediction engine ae done in the online phase of the system to pedict use s futue equests. Classifying of use s cuent activities based on navigation pattens in the paticula web site is the main objective of the pediction engine. In addition, ceating a list of ecommendations web pages as pediction is anothe objective in online phase. In the following, we state the online components. Figue 1: Model of the system As the fist objective, the pediction engine is used to classify use navigation pattens and pedicts uses futue equests. Fo this pupose, we popose a novel appoach to classify cuent use activity. In ode to classify use s active session, we look fo the navigation patten that includes the lage numbe of simila web pages in the session. Patten seach appoaches can be utilized to find simila web pages between the cuent active session and navigation pattens. The longest common subsequence (LCS) algoithm is to find the longest subsequence common to all sequences in a set of sequences (often just two). In the next section, we descibe about the longest common Subsequence algoithm. The second objective of this component is computing a ecommendation set fo the cuent session, consisting of links to pages that the use may want to visit based on simila usage pattens. The ecommendation set essentially epesents a "shot-tem" view of potentially useful links based on the use's navigational activity though the site. These ecommended links ae then added to the last page in the session accessed by the use befoe that page is sent to the use bowse. IV. USER CLASSIFYING THROUGH LONGEST COMMON SUBSEQUENCE The poblem of compaing two sequences α and β to detemine thei similaity is one of the fundamental poblems in patten matching. One of the basic foms of the poblem is to detemine the longest common subsequence (LCS) of α and β. The LCS sting compaison metic measues the subsequence of maximal length common to both sequences [12]. Fomally, given a sequence α = α 1, α 2,, α n, anothe sequence γ = ϒ 1, ϒ 2,, ϒ n is a subsequence of α if thee exists a stictly inceasing sequence j 1, j 2,, j n of indices of α such that fo all i=1,2,..,l, we have α ji =ϒ i. Given two sequence α and β, we say that γ is common subsequence of α and β if γ is a subsequence of both α and β.we ae inteested in finding the maximum-length o longest common subsequence (LCS) given two paths o sequence of page-visits α = α 1, α 2,, α n, β = β 1, β 2,, β m. The LCS has a well-studied optimal sub-stuctue popety

3 as given by the following: Theoem 1: Let α = α 1, α 2,, α n and β = β 1, β 2,, β m be sequences, and let γ = ϒ 1, ϒ 2,, ϒ n be any LCS of α and β. If α n = β m, then ϒ l =α n = β m and γ l 1 is a LCS of α and n 1 β m 1. If α n β m, then ϒ l α n implies γ is a LCS of α and n 1 β. If α n β m, then ϒ l β m implies γ is a LCS of α andβm 1. whee αn 1 = α 1,α 2,, α n -1, β = β m 1 1, β 2,, β m-1 and γ l 1 = ϒ 1, ϒ 2,, ϒ n-1. The poof of this theoem can be found in [13]. Efficient ecusive algoithms to compute the LCS exist using this popety of the LCS [14]. We shall not go into the details of the algoithms since a lot of liteatue aleady exists on the topic[14-16]. Definition 1: Let s1 and s2 be two sequences. LCS (s1, s2) is the size of the longest common subsequence between s1 and s2. The degee of similaity between s1 and s2 is defined as below: 2 LCS( S1, S2) Sim LCS = (1) S1 + S2 Patten seach algoithm can be utilized to find navigation pattens based on the cuent use activities to pedict and ecommend use futue s equest. We apply a patten seach algoithm namely Longest Common Subsequences (LCS) in the ecommendation pat of the system. In this pape, thee ae seveal steps to ceate ecommendations set based on the cuent use s session in the online phase of the system which will be explained below. A. Data Peteatment fo ecommendation Pepocessing fo both cuent active session and navigation pattens is done in the fist step of the pediction engine. In this step the pediction engine pefoms is pepaing data fo applying LCS algoithm with take into account the efficiency of the algoithm. In this study, the cuent active session S is epesented as a vecto: S = P1, P 2,..., Pm (2) whee Pi=n, and n is a unique numeic value that we assigned to each web pages in the offline phase. If use visits a web page, the system eplaces it with a pedefined unique numeic value. Fix-size sliding window is utilized ove the cuent active session to captue the cuent use s activities. We call this sliding window, the use s active session window. We conside the mean of web pages in each session of dataset as use s active session window. Futhemoe, in the poposed algoithm, web pages inside the use s active session window have to put in ode accoding to the numeic values. Hee, we pesent each cluste that ceated in the offline phase as set of navigation pattens. = 1, 2,..., m (3) whee i, is a set of k web pages as a navigation patten and show as below : n pi = P1, P 2,..., Pk whee 1 i n, and pi is a web pages in a navigation patten. Moeove, i have to put in ode as same as use s active session window. B. Use classifying based on LCS algoithm Thee ae two sets, navigation pattens (4) i and active session window S as iut of this step. Classifying algoithm attempts to find a navigation patten (Cluste) by utilizing longest common subsequences algoithm. A navigation patten with the highest degee of similaity is found accoding to the LCS algoithm to pedict next use s activities and ceate a ecommendation set. C. Pedict use next s intention In this step of the algoithm, a set of web pages shows to the use as ecommendation set. A ecommendation engine attempts to show a set of web pages to the cuent use afte the system finds a navigation patten with the highest degee of similaity. Meanwhile, web pages in the ecommendation set ae anked in tems of degee of connectivity between web pages in adjacency matix M that ceated in the offline phase. Moeove, fo impoving the quality of ecommendation, the ecommendation engine shows only the web pages by highest degee of connectivity and the est of web pages wee not be consideed in the ecommendation set. The new ecommendation set is ceated by next use s movement in the web site. In this case, afte each use s activity, new use session window is ceated. V. SYSTEM EVALUATION A vaiety of techniques ae used to measue the pefomance of the ecommende systems. Some expeimentations have been done fo chaacteize the quality of the ecommendation. In ode to evaluate effectiveness of the poposed system seveal tests should be conducted fo both online phase and offline phase. Measuing the accuacy of the pedictions and pedicting in the ecommende system needs to chaacteize the quality of the esults obtained. As we descibed evaluation method in the offline phase, to measue the quality of ecommendation, we use second half of the dataset afte the dataset divided into two halves; taining set and evaluation set. Each navigational patten i (a session in the dataset) in the evaluation set is divided into two pats. The fist n pageviews in i ae used fo geneating pedictions, wheeas, the emaining pat of i is used to evaluate the geneated pedictions. The active session window is the pat of the use s navigational pattens used by the pediction engine in ode to poduce a pediction set. We call this pat of the navigational patten the active

4 session with espect to, denoted by as. The pediction engine takes as and a ecommendation theshold τ as iuts and poduces a set of pageviews as a pediction list. Recommendation theshold τ is the MinFeq. We denote this pediction set by P (as, τ). The set of pageviews P (as, τ) can now be compaed with the emaining n, pageviews in. We denote this pat of by eval. Ou compaison of these sets is based on 3 diffeent metics, namely, accuacy, coveage and F1 measue. The accuacy of pediction set is defined as: Accuacy( as whee,, τ )) = as, τ ) eval as, τ ) (5) as, τ ) eval is numbe of web pages that these ae common in the pediction list and evaluation set. Accuacy is Numbe of elevant web pages etieved divide by the total numbe of web pages in ecommendations set. In the othe hand, accuacy measues the degee to which the pediction engine poduces accuate ecommendations. Anothe evaluation paamete in the online phase is coveage that is defined as: as, τ ) eval Coveage( as, τ )) = eval (6) Coveage is numbe of elevant web pages etieved divide by the total numbe of web pages that actually belong to the use sessions. In the othe hand, coveage measues the ability of the pediction engine to poduce all of the pageviews that ae likely to be visited by the use. The F1 measue attains its maximum value when both accuacy and coveage ae maximized. Finally, fo a given pediction theshold τ, the mean ove all navigational patten in the evaluation set is computed as the oveall evaluation scoe fo each measue. 2 Accuacy( as, τ )) Coveage( as, τ )) F1 = Accuacy( as, τ )) + Coveage( as, τ )) (7) All paametes attempt to measue the quality of ecommendation in the ange of MinFeq between and 1. VI. EXPERIMENTAL EVALUATION In ode to evaluate the pefomance of the poposed system, expeimentation has been conducted fo pediction of the use s next equest by classifying algoithm based on longest common subsequence. All evaluation tests have un on a dual pocesso Intel Coe Duo CPU 2.4 GHz with 3.23 GBytes of RAM, opeating system Windows XP. Ou implementations have un on.net famewok 2 and VB.net and C#.net have been used fo coding the poposed system. Fo ou expeiments, it is necessay to use such a dataset that allows us to evaluate the ecommendations. Ou expeiments have been conducted on DePaul Univesity CTI logs file dataset ( All evaluations fo the ceating use navigation pattens have been done based on the CTI dataset in the ou pevious wok [11]. The length of the active session window is impotant to classify cuent active session in the poposed ecommende system. The aveage numbe of web pages in a session can be used fo consideing the length of active session. As shown in Figue 2 the pecentage of the sessions fomed by a pedefined numbe of pages quickly deceases when the minimum numbe of pages in a session inceases. Moeove, fo CTI dataset, aveage length of a use session is about 3 pages. Theefoe, we still have almost half of all the sessions, we choose this value as the minimum length fo an active session to be classified. % Session in Dataset Sessions Minimum Numbe of Pages in a Session Figue 2: Minimum Numbe of pages in a session In this section, we measue the quality of the ecommendation geneated by pediction engine duing the online phase. Sessions found in one half of the both datasets ae submitted to the pediction engine to classify cuent use s activities and to geneate ecommendation. Subsequently, the ovelapping between the geneated pediction as, τ) and the session pages eval is computed by using expession (5) that intoduce in section 5. Finally, the pecentage of all pedictions figues out as quality of the pedicting in the poposed ecommende system. In this study, we evaluate the quality of the ecommendations between the navigation patten ceated by poposed method [11] and classifying method based on LCS algoithm by the pevious algoithms [11]. Thee paametes have been measued to veify the quality of the pedictions; accuacy, coveage and F1. Pecentage of the Accuacy Accuacy MinFeq Figue 3: Accuacy of the ecommendations CTI Pev. Method (CTI) LCS (CTI) Figue 3 depicts the accuacy of the poposed system fo

5 MinFeq anging fom to 1 fo the CTI dataset. Minimum fequency is a paamete fo filteing the weights which ae below a constant value, named as MinFeq. The edges of the gaph whose values ae less than MinFeq ae inadequately coelated and thus they ae not consideed by the DFS gaph seach algoithm. This paamete is used in the navigation patten mining as theshold. The pecentage of the accuacy fo the poposed system achieves the best esults when we choose the value of the MinFeq to be aound.55 fo CTI dataset. The esults illustate the appoach can impove the accuacy of the ecommendation in a geat deal of diffeence values of the MinFeq against the pevious wok. Figue 4 depicts the coveage of the poposed system fo MinFeq anging fom to 1. The coveage achieves high pecentage fo the lowe values of the MinFeq. In this case, fo the lowe values of the MinFeq by classifying cuent use s activities, the pediction engine can find the clustes with lage size that many web pages inside the cluste belong to the actual use s activities. Moeove, simila easoning can be used fo the highe values of the MinFeq that decease the pecentage of the coveage. The esults illustate the appoach can impove coveage of the ecommende system in compaison with pevious wok. Pecentage of the Coveage Coveage Pev. Method (CTI) LCS (CTI) MinFeq Figue 4: Coveage of the ecommendations Figue 5 depict the F1 measue fo MinFeq anging fom to 1 fo the CTI dataset. In the most time, F1 achieves highe pecentages fo the poposed system than the pevious wok. Fo all expeiments, we un paied t-test with 95% confidence.paied t-test is used to compae means on the same o elated subject ove time o in the diffeing cicumstances. On the othe hand, the t-test assesses whethe the means of two goups ae statistically diffeent fom each othe. This analysis is appopiate wheneve you want to compae the means of two goups. The assumption fo using the paied t-test is that the obseved data ae fom the same subject o fom a matched subject and ae dawn fom a population with a nomal distibution. In this study, a paied t-test is caied out to compae the expeimental esults fo the F1 measue. The mean of F1 measue fo the CTI dataset is 19.5 fo the poposed appoach and 7.9 fo the pevious wok. The two-tail p-value fo the paied t-test is.6 that achieves significant value (p-value<=.5). In this pape, we also test the scalability based on the cuent active session (window size). We stat the expeiment with window size 2, and accoding to this size, we measue the accuacy of the WebPUM system. Moeove, in the each tail we add one page to cuent use active session (window). On the othe hand, window size inceases in each tail. As we shown in Figue 6, the pecentage of the accuacy inceases, if the window size changes between 2 and 13. In the most time, the accuacy is stable in the pevious wok accoding to the diffeence window size. Howeve, the weakness of inceasing the window size is that the numbe of active session which can be consideing fo the ecommendation system is decease and the system cannot pepae pediction fo a geat deal of active sessions. Pecentage of the Accuacy Pev. Method(CTI) LCS(CTI) Size of active session Pecentage of the F F1 Pev. Method (CTI) LCS (CTI) MinFeq Figue 5: F1 measue of the ecommendations Figue 6: The accuacy based on size of active session (window size) Based on expeimental esults, it indicates that ou appoach to classify cuent use s activities to pedict use s next equest can impove the quality of the pedictions in the web usage mining ecommende systems. VII. CONCLUSIONS AND FUTURE WORK In this pape, we advanced a system and poposed a novel appoach to classify the use navigation patten fo online pedicting uses futue intentions though mining of web seve logs. To classify cuent use activities, we used longest common subsequences algoithm to pedict use nea futue movement. We used some evaluation methodologies that can evaluate the quality of the clustes found and quality of the ecommendations. The expeimental esults show that ou

6 appoach can impove the quality of clusteing fo use the navigation patten and quality of ecommendation fo the CTI dataset. Moeove, by using LCS, we achieved moe accuate ecommendation fo long pattens of the cuent use activities in the paticula web sites. Thee ae some aspects in that can be impoved in ou system. Fo instance, we can take into account the semantic knowledge about undelying domain to impove the quality of the ecommendation. On the othe hand, integating semantic web and web usage mining can achieve best ecommendations in the dynamic huge web sites. Moeove, we would pefect the system and let it seves fo actual uses to the best of its abilities. REFERENCES [1] P. Resnick and H. R. Vaian, "Recommende systems," Communications of the ACM, vol. 4, pp , [2] R. Cooley, B. Mobashe, and J. Sivastava, "Web mining: infomation and patten discovey on the Wold Wide Web," 1997, pp [3] J. Wang and I. NetLibay, Encyclopedia of Data Waehousing and Mining: Idea goup Refeence, 26. [4] J. Sivastava, R. Cooley, M. Deshpande, and P. N. Tan, "Web usage mining: discovey and applications of usage pattens fom Web data," ACM SIGKDD Exploations Newslette, vol. 1, pp , 2. [5] M. Jalali, N. Mustapha, M. N. B. Sulaiman, and A. Mamat, "OPWUMP: An Achitectue fo Online Pedicting in WUM-Based Pesonalization System," Communications in Compute and Infomation Science,Advances in Compute Science and Engineeing,Spinge Belin Heidelbeg, vol. 6, pp , 28. [6] M. Jalali, N. Mustapha, N. B. Sulaiman, and A. Mamat, "A Web Usage Mining Appoach Based on LCS Algoithm in Online Pedicting Recommendation Systems," in 12th Intenational on Infomation Visualisation,IV'8, London, UK, 28, pp [7] B. Mobashe, R. Cooley, and J. Sivastava, "Automatic pesonalization based on Web usage mining," Communications of the ACM, vol. 43, pp , 2. [8] M. Nakagawa and B. Mobashe, "A hybid web pesonalization model based on site connectivity," 23, pp [9] R. Baaglia and F. Silvesti, "Dynamic pesonalization of web sites without use intevention," Communications of the ACM, vol. 5, pp , 27. [] R. Baaglia and F. Silvesti, "An Online Recommende System fo Lage Web Sites," 24, pp [11] M. Jalali, N. Mustapha, M. N. Sulaiman, and A. Mamat, "Web Use Navigation Patten mining appoach based on Gaph Patitioning algoithm " Jounal of Theoetical and Applied Infomation Technology vol. 4, pp , 28. [12] A. Apostolico, "Sting editing and longest common subsequences," Handbook of Fomal Languages, vol. 2, pp , [13] T. H. Gomen, C. E. Leiseson, R. L. Rivest, and C. Stein, "Intoduction to Algoithms," MIT Pess, vol. 44, pp. 97,138, 199. [14] V. Dancik, Expected Length of Longest Common Subsequences: Univesity of Wawick, [15] A. V. Aho, D. S. Hischbeg, and J. D. Ullman, "Bounds on the complexity of the longest common subsequence poblem," 1974, pp [16] D. S. Hischbeg, "Algoithms fo the Longest Common Subsequence Poblem," Jounal of the ACM (JACM), vol. 24, pp , 1977.