Rapid Frequent Pattern Growth and Possibilistic Fuzzy C-means Algorithms for Improving the User Profiling Personalized Web Page Recommendation System

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1 Received: November 21, Rapid Frequet Patter Growth ad Possibilistic Fuzzy C-meas Algorithms for Improvig the User Profilig Persoalized Web Page Recommedatio System Sipra Sahoo 1 * Bikram Kesari Ratha 2 1 Departmet of Computer Sciece & Egieerig, Siksha O Ausadha Uiversity, Idia 2 Departmet of Computer Sciece & Egieerig, Utkal Uiversity, Idia * Correspodig author s sipraphd2017@gmail.com Abstract: Web page recommedatio systems are used to recommed the future web page views to World Wide Web (WWW) users. Existig persoalized web page recommedatio systems are still limited to several problems such as, cold start, sparse data structures (Sparsity), ad o diversity i the set of recommeded items (Cotet Overspecializatio). I order to overcome these difficulties, we proposed a ew persoalized web recommedatio system by cosiderig the differet cotributios of the traiig samples (MSNBC dataset, ad castigs techology iteratioal dataset). At first, the web pages are coverted ito umber of sequeces. A effective clusterig algorithm called as Possibilistic Fuzzy C-Meas (PFCM) is employed to idetify the eighbourhood of each user profile. The the associatio rule miig techique, Rapid Frequet Patter Growth (RFPG) algorithm is used for miig the frequet web pages. Fially, the experimetal outcome shows that the proposed approach delivers the high priority of web pages ad also recommeds the related web pages. Fially, the experimetal outcome shows that the proposed approach improved accuracy i web page recommedatio up to 50-60% compared to the existig methods. Keywords: Frequet patter growth, Fuzzy c-meas, Rule miig Web page recommedatio system, World wide web. 1. Itroductio Exceptioal growth of a iformatio o the WWW with the developmet of advaced electroic devices has made the web iformatio icreasigly importat i almost everybody s life [1, 2]. People use the iteret to get iformatio o area of iterest, do research related to work or study, get good deals for commodities or travel, icreases the awareess of their surroudigs, ad to get the latest ews, etc. [3]. I the recet days, huge amout of web pages, web sites ad web documets are available, so the search egie returs the thousads of related liks to a search query. The outlie of the curret websites has overwhelmed the web users by offerig may choices [4]. Hece, the web users ted to make poor decisios whe surfig the Web, due to a iability of maagig the eormous amout of iformatio. Also, it has become very difficult for users to get the relevat iformatio [5]. I order to extract the useful patters i web usage, a effective system is implemeted amed as a web-page persoalizatio recommedatio system. Web persoalizatio system has become more popular as the users get a relevat liks of various webpages ad websites without searchig repeatedly [6]. The existig persoalized web-page recommedatio systems have some complexities such as the cold start problem, which refers to the situatio whe a ew user or item just eters ito the system [7]. I such cases, it is really very difficult to provide recommedatios as i the case of ew users, there is a very less iformatio about the user ad also for a ew item, o ratigs are available [8, 9]. The persoalized web recommedatio is carried out based o the relatios betwee the products i the previous trasactios ad also aother possibility of recommedatio is based o the results give by user about a product ad similar feedbacks from the differet users [10]. To overcome these problems, a

2 Received: November 21, ew persoalized web recommedatio system is proposed. I this experimetal research, the webpage recommedatio system is performed o the reputed datasets: MSNBC dataset, ad castigs techology iteratioal dataset. After the acquisitio of data, covert the web pages ito umber of sequeces. The PFCM clusterig techique is performed o the umber of sequeces for idetifyig the eighbourhood of each user profile. After idetifyig the user profiles, the associatio rule miig is applied usig the RFPG algorithm for miig the frequet web pages. This proposed model provides the highest priority of web pages ad also recommeds the persoalized web page to the user. This paper is composed as follows. Sectio 2 survey several recet papers o the web-page recommedatio system. I sectio 3, PFCM method is preseted with RFPG methodology for recommedig the related web pages. I Sectio 4, comparative aalysis for proposed web-page recommedatio system usig MSNBC, ad castigs techology iteratioal datasets are preseted. The coclusio is made i the Sectio Literature review Several techiques are suggested by researchers i the web-page recommedatio system. I this sceario, a brief evaluatio of some importat cotributios to the existig literatures is preseted. T.T.S. Nguye, H.Y. Lu, ad J. Lu [11] preseted the ew models such as, otology based model ad coceptual predictio based model. A otology model provides the relatio betwee sematic etwork domais ad web pages. The coceptual model predicts the ext web page request of the users through queryig kowledge bases ad provide a sematic etwork of the web usage kowledge. This model was the combiatio of both domai kowledge ad web usage kowledge. This iformatio retrieval system hadles the oe by oe user request. Whereas, multiple user request was difficult to hadle i this system. R. Geetharamai, P. Revathy, ad S.G. Jacob [12] proposed a ew model amed as Apriori Prefix Tree (APT) algorithm for web page predictio. The objective of this literature was to predict the probable subsequet page i the web pages based o their avigatig behaviour. APT algorithm was used to mie the commo patters, which were ivestigated based o the rule cosequets ad atecedets. I the higher user recommedatio system, less user logs were received, so the performace of web predictio accuracy was decreased. R. Katarya, ad O.P. Verma [13] illustrated a ew web-based recommeder system, which was based o the sequetial iformatio about user s avigatio o the web pages. I this literature, Fuzzy C Meas (FCM) clusterig techique was used to provide the recommeder system sequetially. Usig FCM, the Top-N clusters were achieved, which were further used for idetifyig the similar users for a target user. The, calculate the weight for each page category ad fially determie the accuracy of the system. This approach was ot cocetrated o the cotet of the web page ad also FCM was ot suitable for a ew user, because it requires at least six web logs per user. N.T.S.H. Rizvi, ad R.R. Keole [14] preseted a itelliget web recommedatio system usig a term patter frequecy miig techique for predictig the ext web page. A desktop search utility uses the web usage miig process for fidig the term patters i web query data, which was used for predictig the possible ext pages from browsig sessios i a very less time. The proposed framework recommeds the best web pages based o the user queries usig the google search egie. So, the web page recommedatio for a multiple website at the same domai was required. S.P. Malarvizhi, ad B. Sathiyabhama [15] has preseted a ew methodology called as a T+ weight tree algorithm for fidig the frequet page-sets. The proposed T+ weight tree algorithm fids the frequet page-sets based o the weights i a sigle sca of the database. The time elapsed for fidig all the subsets of a set was more, so a effective process was required to reduce the time. The proposed methodology was much more beeficial to fid the frequet page-sets. I the weighted associatio rule miig techique updatig process ot occurred, so it does t cosider the behavioural patter of web pages. A.S. Tewari, ad A.G. Barma, [16] proposed a recommedatio system usig cotet based collaborative filterig, associatio rules ad opiio miig. Whereas, the cotet based collaborative filterig was used to moitor the user shoppig behaviour. Also, the proposed methodology fids the like-mided people with the target user that may cooperate with each other i the form of items ratigs usig collaborative filterig. The proposed methodology utilizes associatio rule miig for the aalysis of curret market tred, which geerates associatio rules oly from those items that are liked by the users. I this literature, the proposed methodology utilizes ow uique weight that summarizes the reviews ad geerates the weights for each item based o customers reviews. These weights help i estimatig the popularity of a item amog customers. If there was ot eough

3 Received: November 21, iformatio to build a solid profile for a user, the recommedatio could ot be provided correctly. V. Vellaichamy, ad V. Kalimuthu, [17] developed a hybrid collaborative movie recommeder system, which was the combiatio of both Fuzzy C Meas clusterig (FCM) ad bat optimizatio techique. This combied methodology improves the recommedatio quality. FCM clusterig was utilized to cluster the users ito differet groups. O the other had, bat algorithm was utilized to obtai the iitial positio of the clusters. At last, the proposed methodology creates the movie recommedatio for target users. The major drawback of FCM was limited cotet aalyser. If the cotet does ot cotai eough iformatio to discrimiate the items precisely, the recommedatio was ot precisely at the ed. To overcome the above metioed drawbacks, a effective algorithms PFCM ad RFPG is combied for improvig the user profilig persoalized web page recommedatio system. 3. Proposed methodology The proposed persoalized web page recommedatio system cosists of five steps such as, data acquisitio, pre-processig, similarity measure, clusterig ad patter miig. A geeral block diagram of persoalized web recommedatio system is represeted i Fig. 1. The brief descriptio about the proposed methodology is determied below. 3.1 Pre-processig I the iitial stage of the persoalized web page recommedatio system, the web logs ad web cotet of user data are take from the stadard bechmark datasets such as, MSNBC dataset ad castigs techology iteratioal dataset. I the preprocessig phase, the web logs data or web pages are coverted ito umber of sequeces ad also idetifies the user profiles. The, all the similar sequeces are clustered usig PFCM with the help of S3M similarity measure. The S3M similarity measure or set similarity measure cosiders the both cotet as well as the sequece of the iput data for computig the similarity betwee the web users. The S3M cotais two phases: oe is set similarity that quatifies the compositio of sequece ad aother oe is sequece similarity that quatifies the sequetial ature. Sequece similarity is deoted as the order of occurrece of item sets withi two sequeces. Legth of Logest Commo Subsequece (LLCS) with respect to the legth of the logest sequece defies the sequece similarity aspect across two sequeces. For two sequeces A ad B, sequece similarity is give i Eq. (1). SeqSim(A, B) = LLCS(A,B) max ( A, B ) (1) Set similarity is also kow as Jaccard similarity measure, which is defied as ratio of the umber of commo item sets ad the umber of uique item sets i two sequeces. Thus, for two sequeces A ad B, set similarity is give Eq. (2). SetSim(A, B) = A B A B (2) Thus, S3M is costituted as a fuctio of both set similarity ad sequece similarity ad is defied i Eq. (3). S 3 M(A, B) = P LLCS(A,B) max( A, B ) + q A B A B (3) Where, p + q = 1, p, q 0. Here, p ad q is represeted as the relative weights. Oce the similarity betwee the web users are computed, the iitial set of the clusters is formed usig PFCM. 3.2 Possibilistic fuzzy c-meas Figure.1 A Geeral block diagram of the proposed approach The PFCM is a data clusterig techique, where each data poit is a part of the cluster to a level

4 Received: November 21, idicated by the membership grade. I the clusterig module, PFCM is depedet o the reductio of the objective fuctio, which is illustrated i the followig Eq. (4). C m J PFCM (U, T, V) = i=1 j=1(u ij + t )d 2 (x j, v i ) (4) With the followig costraits: c i=1 μ ij = 1, j {1,, } (5) j=1 t ij = 1, i {1,, c} (6) Where, J PFCM is the objective fuctio, U is represeted as the partitio matrix, T is represeted as the typicality matrix, V is deoted as the vector of cluster cetres. The outcome of the objective fuctio is achieved usig a iterative approach, where the degree of membership ad the cluster cetres are mathematically represeted i Eqs. (7), (8) ad (9). μ ij = [ t ij = [ v i = c ( dx j,v i k=1 ) 2 dx j,v k ( dx j,v i k=1 ) 2 dx j,v k (u ik m k=1 +t ik)xk k=1(u m ik +t ik ) m 1] 1, 1 i c, 1 j (7) 1] 1, 1 i c, 1 j (8), 1 i c (9) learig procedure α is a depedet expoetial separatio stregth betwee the clusters ad it is updated at every iteratio. The parameter α is represeted i Eq. (10). α = exp ( mi i k v i v k 2 β ) (10) Where, β is represeted as sample variace, mathematically, it is represeted i Eq. (11). β = x j x 2 j=1 Where, x = j= x j (11) The, a weight parameter is itroduced to calculate the commo value of α. Each poit of the database cosists of a weight i relatioship with each cluster. So, the usage of weight fuctio delivers a better classificatio outcome, especially i the case of oise data. The geeral equatio of weight fuctio is determied i Eq. (12). w ji = exp ( x j v i 2 ( x j v 2 ) (12) j=1 ) c/ Where, w ji is deoted as the weight fuctio of the poit j with the class i. The step by step procedure of PFCM is represeted i Fig. 2, ad also effectively explaied below, Where, is represeted as the umber of data poits, c is represeted as the umber of cluster ceters, which are described by the coordiates (x j, v i ) ad it is used to calculate the distace betwee cluster ceter ad data sets. PFCM costructs possibilities ad memberships with ormal prototypes ad cluster ceters for every cluster. Choosig the objective fuctio is the importat aspect for the performace of cluster methodology for accomplishig better clusterig. Whereas, the clusterig performace is based o the objective fuctio, which is utilized for clusterig. For developig a effective objective fuctio, the followig set of requiremets are cosidered. Distace betwee the clusters should be reduced. Distace betwee the data poits, which allocated i the clusters should be reduced. The desirability betwee the clusters ad data are modelled by the objective fuctio. Further, the objective fuctio of PFCM is improved by usig drive prototype learig of parameter α. The Figure.2 Step by step procedure of PFCM

5 Received: November 21, Iitializatio: At first, the umber of clusters is furished by the user, which are idetical with respect to every segmet. Calculatio of similarity distace: Whe the umber of clusters is determied, the evaluatio of the distace betwee the cetroids ad data poits for each segmet is carried out. Calculatio of typicality matrix: After the estimatio of distace matrix, the typicality matrix is evaluated, which is obtaied from the PFCM. Calculatio of membership matrix: The evaluatio of the membership matrix M ik is performed by meas of assessig the membership value of data poit, which is gathered from the PFCM. Updatio of cetroid: After the geeratio of clusters, the moderizatio of the cetroids is updated. The relative procedure is performed agai ad agai till the moderized cetroids of each ad every cluster becomes idetical i successive iteratios. To improve the clusterig efficiecy of the proposed method, a ew associatio rule miig techique is developed after the clusterig algorithm ad it is discussed i the below sectio. 3.3 Rapid frequet patter growth by matchig the traversal of the web log usig user query, the the RFPG tree fiishes the whole task ad termiate. The step by step procedure of RFPG tree algorithm is determied below. Algorithm (RFPG-costructio) Iput: MSNBC dataset, ad castigs techology iteratioal dataset Output: RFPG-tree (related web-pages based o user request) Method: Costructio of RFPG-tree Geerate frequet patters 1) Iitially, sca the databases ad covert the web pages ito umber of sequeces (S). The, sort the sequeces (S) ito descedig order as (d), which cotais a list of frequet web pages. Costructio of RFPG-tree 2) Create a root for RFPG-tree, label it as ull. Sca the databases agai, for each iteratio ad arrage the frequet webpages ito descedig order (d), the isert the frequet web-pages ito RFPGtree. Geerate predecessor ad successor for each ode 3) Sca RFPG-tree by predecessor traversal to geerate the predecessor code. Sca RFPG-tree agai by successor traversal to geerate the successor code. I this sectio, the RFPG algorithm is examied o the hypothetical databases for recommedig the related web-pages. Iitially, the RFPG algorithm has a ode-lik field ad a header table for maitaiig the coectio betwee the odes, whose web-pages 4. Experimetal outcome are equal i the tree. If the RFPG algorithm does ot For experimetal simulatio, Net-Beas (versio have such structure meas, it is a simpler prefix tree. 6.2) was employed o PC with 3.2 GHz with i5 The, each ode i the RFPG tree has the successor processor. I order to estimate the efficiecy of the ad predecessor fields, if the odes i the RFPG trees proposed algorithm, the performace of the proposed are oe. The predecessor ode is determied by the method was compared with Sigular Value predecessor traversal of the RFPG tree. Decompositio (SVD) [18] o the reputed datasets: I the predecessor traversal, a ode M is assiged MSNBC dataset, ad castigs techology to the predecessor rak, before all the predecessor iteratioal dataset. The performace of the childre s are traversed from left to right. Also, the proposed methodology was compared i terms of predecessor measures the time, whe ode M is accuracy ad precisio. assessed durig the predecessor traversal. Likewise, the successor odes are determied by usig 4.1 Performace measure successor traversal of the tree. I the successor traversal, from the root ode the cosecutive The relatioship betwee the iput ad output successor childre traverse from left to right, variables of a system uderstad by employig the accordig to the rakig of frequet web pages. At suitable performace metric like precisio. The last, after buildig the RFPG tree, it is utilized for geeral formula for calculatig the precisio of the frequet patter miig, which is the complex ad persoalized web page recommedatio system is recursive procedure. However, RFPG tree is utilized give i Eq. (13) as. for geeratig the predecessor ad successor code of each ode. Later, the related web-pages are idetified Precisio = TP 100 (13) TP+FP

6 Received: November 21, Table 1. Accuracy compariso of existig ad proposed method usig MSNBC dataset Methods No. of predictios Radom SVD [18] PFCM RFPG Figure.3 Performace compariso usig MSNBC dataset Accuracy is the measure of statistical variability ad a descriptio of radom errors. The geeral formula of accuracy for determiig persoalized web page recommedatio system is give i Eq. (14). Accuracy = TP+ TN TP+TN+FP+FN 100 (14) Where, TP is represeted as true positive, FP is deoted as a false egative, TN is represeted as true egative ad FN is stated as a false egative 4.2 Result for MSNBC dataset I this experimetal aalysis, MSNBC dataset is assessed for comparig the performace evaluatio of existig method ad the proposed scheme. I table 1, the accuracy of the proposed ad existig methodology is compared to the five samples ad three predictios. The average accuracy of existig methods (SVD) for each predictio delivers %, 18.42%, ad 27.74%. Likewise, the average accuracy of the proposed method (PFCM- RFPG) for each predictio delivers %, %, ad 80.80%. The performace evaluatio of MSNBC dataset is deoted i Fig Result for castigs techology iteratioal dataset The experimetal aalysis of castigs techology iteratioal dataset is evaluated for comparig the performace of existig ad the proposed method. I table 2, the accuracy of the proposed ad existig methodology is also compared to five samples ad three predictios. The average accuracy of existig methods (SVD) for each predictio delivers %, 27.24%, ad 32.17%. Similarly, the average accuracy of the proposed method (PFCM- RFPG) for each predictio delivers 21.08%, 53.53%, ad 93.16%. The table 1 ad 2 cofirmed that the proposed approach performed effectively compared to the existig methods o the both MSNBC dataset ad castigs techology iteratioal dataset. The performace evaluatio of castigs techology iteratioal dataset is preseted i Fig. 4.

7 Received: November 21, Table 2. Accuracy compariso of existig ad proposed method usig castigs techology iteratioal dataset Methods No. of predictios Radom SVD[18] PFCM RFPG Figure.4 Performace compariso usig castigs techology iteratioal dataset Table 3 preseted the performace of various clusterig (M) usig PFCM- RFPG approach for improvig the persoalized web page recommedatio system. Table 3 clearly shows that the PFCM- RFPG approach improves the precisio i web page recommedatio system up to 50 % compared to the existig method i the both MSNBC dataset ad castigs techology iteratioal dataset. R. Mishra, P. Kumar, ad B. Bhasker [18] proposed a ew recommedatio system usig similarity upper approximatio ad SVD for geeratig the recommedatios for users. The graphical represetatio of precisio compariso is represeted i Figs. 5 ad 6. Table 3. Precisio compariso of existig ad proposed method Methods MSNBC dataset Castigs techology iteratioal dataset Trails First Secod Third First Secod Third SVD [18] M= M= M= Radom PFCM- RFPG M= M= M= Radom

8 Received: November 21, Figure.5 Precisio compariso for MSNBC dataset 5. Coclusio Figure.6 Precisio compariso for castigs techology iteratioal dataset I this experimetal research, a effective persoalized web page recommedatio system is proposed usig PFCM-RFPG scheme for recommedig the related web pages to the user. The PFCM method is used as a clusterig frame-work for idetifyig the eighbourhood of each user profile. After idetifyig the eighbourhood profile, the RFPG algorithm is employed for miig the frequet web pages. I this experimetal aalysis, the proposed approach obtaied 10-50% of improvemet i precisio ad 50-60% of improvemet i accuracy of web page recommedatio for MSNBC ad castigs techology iteratioal dataset, respectively. While comparig with existig work, the proposed approach showed effective results i terms of accuracy ad precisio. The web page recommedatio accuracy is more sigificat i PFCM-RFPG tha the previous methodologies. I the future work, for further improvig the web page recommedatio accuracy, we will develop a ew associatio rule miig approach with a adaptive clusterig methodology.

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