Web Page Recommendation Approach Using Weighted Sequential Patterns and Markov Model

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1 Global Journal of Computer Science and Technology Volume 2 Iue 9 Verion.0 April 202 Type: Double Blind Peer Reviewed International Reearch Journal Publiher: Global Journal Inc. (USA) Online ISSN: & Print ISSN: Web Page Recommendation Approach Uing Weighted Sequential Pattern and Markov Model By K. Suneetha & Dr.M.Uha Rani Sree Vidyanekethan Engineering College/JNTU Abtract - Web page recommendation aim to predict the uer navigation through the help of web uage mining technique. Currently, reearcher focu their attention to develop a web page recommendation algorithm uing the well known pattern mining technique. Here, we have preented a web page recommendation algorithm uing weighted equential pattern and markov model. To mine the weighted equential pattern, we have modified the prefixpan algorithm incorporating the weightage contraint uch a, pending time and recent viiting. Then, the weighted equential pattern are utilized to contruct the recommendation model uing the Patricia trie-baed tree tructure. Finally, the recommendation of the current uer i done with the help of markov model that i the probability theory enabling the reaoning and computation a intractable. For experimentation, the ynthetic dataet i utilized to analyze the performance of W-Prefixpan algorithm a well a web page recommendation algorithm. From the reult, the memory required for the W-prefixSpan algorithm i le than 50% of memory needed for PrefixSpan algorithm. Keyword : Web page recommendation, Weighted equential pattern, Prefixpan, Patricia-trie, Markov model. GJCST Claification: I.5.m Web Page Recommendation Approach Uing Weighted Sequential Pattern and Markov Model Strictly a per the compliance and regulation of: 202. K. Suneetha & Dr.M.Uha Rani. Thi i a reearch/review paper, ditributed under the term of the Creative Common Attribution-Noncommercial 3.0 Unported Licene permitting all non-commercial ue, ditribution, and reproduction inany medium, provided the original work i properly cited.

2 Web Page Recommendation Approach Uing Weighted Sequential Pattern and Markov Model K. Suneetha α & Dr.M.Uha Rani σ Abtract - Web page recommendation aim to predict the uer navigation through the help of web uage mining technique. Currently, reearcher focu their attention to develop a web page recommendation algorithm uing the well known pattern mining technique. Here, we have preented a web page recommendation algorithm uing weighted equential pattern and markov model. To mine the weighted equential pattern, we have modified the prefixpan algorithm incorporating the weightage contraint uch a, pending time and recent viiting. Then, the weighted equential pattern are utilized to contruct the recommendation model uing the Patricia trie-baed tree tructure. Finally, the recommendation of the current uer i done with the help of markov model that i the probability theory enabling the reaoning and computation a intractable. For experimentation, the ynthetic dataet i utilized to analyze the performance of W-Prefixpan algorithm a well a web page recommendation algorithm. From the reult, the memory required for the W-prefixSpan algorithm i le than 50% of memory needed for PrefixSpan algorithm. Keyword : Web page recommendation, Weighted equential pattern, Prefixpan, Patricia-trie, Markov model. I. Introduction S equential pattern mining, an advance of aociation rule mining, i an imperative ubject of data mining, often applied for extracting the ueful information [9]. Sequential pattern mining algorithm deal with the problem of determining the frequent equence in a given databae [6]. Sequential pattern i a equence of itemet that often occur in a pecific order and thu, all item in the ame itemet are expected to encompa the ame tranaction time value or within a time gap. Each equence have a temporally ordered lit of event, wherein each event i a compilation of item (itemet) occurring imultaneouly. The temporal ordering among the event i induced by the abolute timetamp aociated with the event [0]. Generally, all the tranaction of a cutomer are collectively viewed a a equence, called cutomer - equence, where each Author α : Sr. Lecturer Dept. of MCA, SVEC, Tirupati Andhrapradeh, INDIA umauni.k@gmail.com Author σ : Aoc.Profeor & HEAD, Dept. of Computer Science, SPMVV, Tirupati, Andhrapradeh, INDIA muha_rohan@yahoo.com tranaction i repreented a an itemet in that equence and all the tranaction are cheduled in a particular order baed on the tranaction-time [8]. Recently, there ha been a ubtantial interet in uing equential mining approache to contruct web page recommendation ytem. Here, we have preented a web page recommendation algorithm uing weighted equential pattern and markov model. The overall proce of page recommendation baed on Web uage mining conit of three phae: data preparation, mining of weighted equential pattern and recommendation. Data preparation: Thi tep conit of, (i) identifying the different uer eion from the uually very poor information available in web log file and (ii) recontructing the uer navigation path within the identified eion. Mining of weighted equential pattern: The traditional equential pattern mining problem i extended by allowing a weight to be aociated with each page in a uer eion to reflect interet of each page within the uer eion. In turn, thi provide with an opportunity to aociate a weight parameter with each page in a reulting equential pattern, which called a weighted equential pattern (WSP). Recommendation: After mining the weighted equential pattern, the patricia-baed tree i contructed. From the Patricia tree, a recommendation model i developed baed on markov model for prediction of uer to find web page they want to viit. II. Related work Literature preent a lot of web page recommendation algorithm baed on web ued mining, collaborative filtering, and rule-baed filtering. Here, we portray ome recent work preented in the literature for web page recommendation. Forati, R et al [3] have developed an effective and calable approach to deal with the web page recommendation problem. Here, a ditributed learning machine ha been employed to learn the performance of previou uer and to cluter the page baed on learned pattern. Dealing with unviited or recently added page wa one of the difficult and challenging tak in recommendation ytem. A they would never be April 202 Global Journal of Computer Science and Technology Volume XII Iue IX Verion I 202 Global Journal Inc. (US)

3 Web Page Recommendation Approach Uing Weighted Sequential Pattern and Markov Model April Global Journal of Computer Science and Technology Volume XII Iue IX Verion I recommended, it i indipenable to provide a chance for thee eldom viited or recently added page to be incorporated in the recommendation et. By conidering thi problem, a Weighted Aociation Rule mining algorithm ha been preented for the recommendation purpoe. Alo, a HITS algorithm ha been exploited to extend the recommendation et. Furthermore, they have analyzed the propoed algorithm under variou etting, and revealed the efficiency of thi approach in enhancing the overall quality of web recommendation. Yicen Liu et al [5] have introduced an automatic tag recommendation algorithm that ha been employed in the large-cale and real-time data proce uccefully and efficiently. Mot of the prior reearche on tag uggetion have focued on initially, dicovering the relationhip between teting and training data and then, aigning the top ranked tag of the mot related training data to the teting object. But, they not paid any attention in determining the internal relationhip between the tag and weblog. In their current reearch, more than 43% of tag, which have been labeled by weblog uer, have really been employed in the body of the text. In the meantime, the term frequency ditribution, the paragraph frequency ditribution, and the firt occurrence poition of tag were diimilar from the one of non-tag in the text. A well, the tag of a weblog have been aigned in two tep. Initially, ome probability ditribution of the word attribute have been trained through the labeled training weblog, and ome keyword of a teting weblog have been extracted a one part of the tag baed on the probability ditribution. Subequently, with the aid of Latent Semantic Indexing (LSI) model, the other part of the tag have been obtained from the firt part one. Experiment conducted on an extenive tagging dataet of weblog 2 have confirmed that the average tagging time for a new weblog wa le than 0.02 ec, and more than 74% of teting weblog have been properly labeled by mean of the top 5 tag. An approach for recommendation of unviited page ha been preented by Forati, R et al []. They have focued on the recommender ytem baed on the uer navigational pattern and provided proper recommendation to cater to the current need of the uer. The group of uer with analogou browing pattern ha been identified by employing an offline data preproceing and clutering technique. The experiment conducted on real uage data from a commercial web ite have demontrated a coniderable enhancement in the recommendation efficiency of the propoed ytem. Web Peronalization i viewed a an application of data mining and machine learning approache to create model of uer behavior that can be applied to the tak of forecating the uer need and adapting future interaction with the eventual goal of enhanced uer atifaction. An extenive overview of intelligent method for Web Peronalization ha been preented by Sarabjot Singh Anand and Bamhad Mobaher [2]. They have tudied the tate-of-the-art in Web peronalization. Initially, a depiction of the peronalization proce and a claification of the current technique to Web peronalization have been preented. Alo, they have dicued the different ource of data available to peronalization ytem, the modeling technique utilized, and the current technique to analyze thee ytem. Numerou challenge faced by the reearcher in developing thee ytem and alo the olution to thee challenge propoed in literature have been decribed. They have concluded with a dicuion on the open challenge that mut be addreed by the reearch community if thi technology i to create a poitive impact on uer atifaction with the Web. Due to the increaing number of Web ite uch a e-buinee contain a huge number of page, uer find it very hard to wiftly reach their own target page. Thu, Hirohi Ihikawa et al [5] have propoed two approache to Web uage mining a a key olution to thee problem. Firt of all, an efficient recommendation ytem called the L-R ytem ha been depicted, which create uer model through claifying the Web acce log and by mining acce pattern baed on the tranition probability of page accee, and then, recommend the ignificant page to the uer baed on both the uer model and the Web content. The prototype ytem ha been analyzed and obtained the poitive effect. Secondly, another approach ha been employed for creating uer model that cluter the Web acce log baed on the acce pattern. Moreover, the uer model ait to find the unexpected acce path correponding to ill-formed Web ite deign. In addition, Daniel Mican and Nicolae Tomai [2] have propoed WRS, architecture for robut web application. Within the tructure, uage data wa being implicitly obtained by data collection ub-module. Here, the uage data ha been extracted, online and in real time, via a proactive technique. They have efficiently exploited aociation rule mining among both frequent and infrequent item for ucceful pattern dicovery. Thi wa due to the fact that the pattern dicovery module tranactionally procee uer' eion and employ incremental torage of rule. Alo, they have proved that the Wie Recommender Sytem (WRS) ha been traightforwardly implemented within any web application, becaue of the efficient integration of the three phae into an online tranactional proce. 202 Global Journal Inc. (US)

4 Web Page Recommendation Approach Uing Weighted Sequential Pattern and Markov Model III. Propoed algorithm to web page recommendation baed on weighted equential acce pattern Web page recommendation i ignificant reearch over the pat decade due to it real world application. With the intention of real world applicability, we have developed an approach for web page recommendation uing weighted equential pattern and markov model. Here, the traditional equential pattern mining algorithm i modified ignificantly by incorporating the ignificant meaure to mine more ueful pattern. Then, the markov model decribed in [4] i ued to recommend the web page. The block diagram of the propoed approach for web page recommendation i given in Figure. The important tep for generating recommendation to the uer i a follow, Data Preproceing W-PrefixSpan for mining of weighted equential web acce pattern Building Pattern tree model Generation of recommendation uing markov model Block diagram Uer Web page recommendation Matching Markov model Hitoric Data Data Preproceing W_ Prefix pan for Weighted equential Fig. : Block diagram of the propoed approach to web page recommendation a) Data Preproceing Thi ection decribe the preproceing tep of web log file that i the input of the propoed web page recommendation approach. In general, the web log file conit of, IP addre, acce time, HTTP requet method ued, URL of the referring page and brower name (for an example, Web erver log file: [23/Feb/202:08:7:25] GET / HTTP/. IE/7.0 Window 07 ). The initial proce of the propoed web page recommendation approach i to preproce the R Tree Contruction web log file uch a way that the mining proce hould be applied. Here, we make ue of the equential pattern mining proce o there i a need to convert the web log file into the equential databae that hould be in the proper format to mine the weighted equential pattern. Uer Identification: Uer identification i an important tep for contructing the equential databae. IP addre and uer eion are utilized here to track a unique uer from the web log file. Unique IP addre i a new uer but at the ame time, the uer eion hould be fixed for particular time period. If the uer eion i reached to a particular duration for the ame IP addre, then the new eion i acted like new uer. Baed on thi, uer tranaction i formed from the web log file. Weighted Sequential databae generation: Once the uer tranaction are identified, the weighted equential databae i generated including the equence of web page viited by the uer, time pent by the uer on correponding web page and it recent information. Let aume the web log databae D having IP addre, acce time, HTTP requet method ued, URL of the referring page and brower name. After applying the data preproceing tep, we generate the weighted equential databae that i repreented a follow, W in which, i belong to the et of uer and j ignifie the et of page viited by the correponding uer. Here, every element of w contain three tuple w = ( p,, r ) in which the firt tuple belong to the web page, the econd one belong to the time pent within that page and the third one belong to whether it recent one or not. For example, w (, = p 20, 0) i a tuple, which denote that the uer pent 20 econd on page p and 0 ignifie that the page p i not recently acceed by the uer. b) W-Prefixpan for Mining of Weighted Sequential Web Acce Pattern Once the weighted equential databae i contructed, the mining procedure i carried out to find the intereting equential pattern. Here, PrefixSpan [7] i modified a W-PrefixSpan (Weighted-PrefixSpan) by incorporating the pending time and recent view into the mining procedure. The weightage meaure aumed in the propoed W-PrefixSpan algorithm i pending time and recent view. The two apect taken for providing the weightage of the equential pattern are, Spending time: One of the field in the web log data i the duration of the web page which i viewed by the uer. Generally, time pent by the uer within a particular page i neceary to identify the importance of web page. From the web page which having long duration, we can conclude that thi particular web page ha been referred by the uer in a long occaion 202 April 3 Global Journal of Computer Science and Technology Volume XII Iue IX Verion I 202 Global Journal Inc. (US)

5 Web Page Recommendation Approach Uing Weighted Sequential Pattern and Markov Model April Global Journal of Computer Science and Technology Volume XII Iue IX Verion I becaue of it worth. Thu, the pending time i an important meaure for the reearcher who are attempting to identify the interet of the uer. So, if we incorporate the time duration into the mining of equential pattern, the intereting relationhip can be found out from the mined equential pattern that can be effectively applied to web page recommendation proce. Recent view: Another ignificant meaure taken for equential pattern mining i recent view that decribe whether the page i acceed recently or not. The reaon behind taking the recent view for mining the equential pattern i that the more importance hould be given for the web page which are acceed recently than the older one. The behavior of the uer urely vary depend on the time o the recent behavior of the uer i ignificant for finding the equence analyi. With the intention of behavior variation over time, the recent view i alo incorporated into the equential pattern mining algorithm to achieve a ubet of more intereting equential pattern (SR-Pattern). W-PrefixSpan algorithm: In thi ection, we decribe an efficient algorithm, W-PrefixSpan, for mining all the SR- pattern from weighted equence databae. The W-PrefixSpan algorithm i developed by modifying the eminent PrefixSpan algorithm, which ue the pattern growth methodology for mining the frequent equential pattern recurively. Let W = p,, r ), ( p,, r ), ( p,, r ) be ( n n n a data equence of weighted databaew, where p i web page, i a pending time and r ignifie the recent view. A equence W = ( p,, r), ( p2, 2, r2 ), ( p m, m, r m ) i aid to be a ub equence of W only if, () W i a ubequence of W, W W (2) t < t < < t 2 m where, t i the time at which p occurred in W, r m. A equence i aid to be SR equence W if and only if, () W i a ubequence of W, (2) the W-upport hould be atified. At firt, the weighted equential databae W i given to the propoed W-PrefixSpan algorithm that dicover the -length weighted equential pattern from the weighted equential databae by canning the databae once. The -SR pattern (pending time with recent view) which atify the predefined upport threhold are mined from the equential databae by imply canning the databae. The W-upport for the - length pattern i computed a follow, W _ up( p) N N p T I = i= N p T i= ( i) * R( i) R( i) Where, N Number of uer tranaction in the weighted equential databae NT Number of tranaction that contain the web page p, R(i) recent information N = T i I ( i) MT i= i i= Where, M T Total number of web page in one tranaction, i pending time Then, the projection databae i formed by projecting the collection of potfixe of mined -SR equence. In projection databae, ' n ' dijoint ubet are generated if the mined -SR pattern contain ' n ' number of equence. Then, the 2-length SR-pattern are mined from the projected databae by computing the weighted upport on the projected databae. Again, the projected databae i formed with the help of mined 2-SR pattern and thi proce i repeated recurively until all SR equential pattern are mined. The following provide a detailed explanation of the important tep involved in the propoed W-PrefixSpan algorithm. Input : A weighted equence databaew, and the minimum upport threhold min_w _ up. Output : The complete et of weighted-equential pattern β. Method : Call W-PrefixSpan (, 0, W ). Subroutine : W-PrefixSpan ( α,l, W α ) Parameter:α : SR-equential pattern; l : the length of α ; W α : the α -projected databae, if α ; otherwie, the weighted equence databae W. Method:. Scan W α once, find the et of SR- pattern uch that a) ' ' can be aembled to the lat element of α to form a SR-equential pattern; or b) can be appended to α to form a SRequential pattern. 2. For each SR- web page, append it to α to form a SR-equential pattern α '. 5. For each α ', contruct α' -projected databaew α, and call PrefixSpan ( α', l +, W ). α 202 Global Journal Inc. (US)

6 Web Page Recommendation Approach Uing Weighted Sequential Pattern and Markov Model c) Building of Pattern Tree Model Once we mine the weighted equential pattern, the pattern tree i contructed uing the procedure defined in [4, 3]. Initially, trie-baed data tructure given in [] i ued to contruct the pattern tree for web page recommendation. Later, the modification wa done by [4, 3], who utilized the patricia-baed data tructure for web page recommendation due to the advantage of particia tructure over the trie tructure. Here, the procedure defined in [4, 3] i applied to the propoed approach for contructing tree tructure. The method for contructing the pattern tree in the propoed web page recommendation approach i a follow: ) Generate an empty root node, 2) Add the mot ub pattern in the SR-equential pattern et into a node next to the root node, 3) Inert the potfixe of pattern into child node only if the current pattern to be inerted i a uper pattern of inerted pattern, 4) Otherwie, current pattern i inerted into the node next to the root node, and 5) Step 3 and tep 4 i repeated for every pattern in the mined SR-pattern et. d) Generation of Recommendation Uing Markov Model Thi ection decribe the markov model utilized for web page recommendation. Here, we make ue of the markov model decribed in [4] that i ued in the identication of the next page to be acceed by the Web ite uer baed on the equence of previouly acceed page. Here, whenever a new uer come to get the recommendation, the equence path of the new uer i matched with the patricia-trie tructure. Then, the ubequent web page whether it may be from ame node or from it child node i retrieved. Now, the equence path of the new uer i ued to find the accurate recommendation uing the probability definition ued in the previou work [4]. Let the input equence viited by the uer be,, 2, n. At firt, the equence,,, 2, n i matched with the patricia-trie tructure that can provide the matched reult like,, 2, n, n +, k,, 2, n, n +, l and, 2, n, n +, m. Every equence ha the weighted upport value that i given in the node of contructed tree. Then, the probability of computation i carried out to find the mot important equence for the uer. The following equation provide the probability of etimated value for the matched reult. () n+ n (2) n+ n (3) n+ { n+ 2 n n+ m 2 n = { P( n+ = (, 2, n, n+, k ), 2, )} = { P( n+ = (, 2, n, n+, l ), 2, )} = P( = (,,,, ),, )} Thi probability, pro( n+ ), i etimated by uing all equence of all uer in tree tructure contructed from the weighted equential databae W. i j P( n = (, 2, n, n+, W _ up(, 2, n) W _ up(,,,, ) 2 n+ ),, + m 2 n = n Then, the final recommendation i baed on the following equation: () (2) (3) arg max{,, } IV. n+ = n+ n+ n+ m Reult and dicuion Thi ection preent the reult obtained from the experimentation and it detailed dicuion about the reult. The propoed approach of web page recommendation i experimented with the ynthetic dataet and the reult i evaluated with the preciion, applicability and hit ratio. a) Experimental Set Up and Dataet Decription The propoed web page recommendation approach i implemented in Java (jdk.6). Here, the ynthetic dataet i generated a like the ame format of real dataet and the performance of the propoed approach i evaluated with the evaluation metric. The generated ynthetic dataet i divided into two part uch a, Training dataet (It i ued for building the pattern tree model and tet dataet (It i ued for teting the web recommendation approach). b) Performance of the W-Prefixpan Algorithm Here, the performance of the W-prefixSpan algorithm i analyzed with the execution time, memory uage and pattern mined. At firt, the training data i given to the prefixspan and W-prefixSpan algorithm to mine the equential pattern and then, the mining performance of the algorithm are analyzed. The value obtained from the experimentation are plotted a graph, hown in figure 2, 3 and figure 4. Fig. 2 : Computation time ) April Global Journal of Computer Science and Technology Volume XII Iue IX Verion I 202 Global Journal Inc. (US)

7 Web Page Recommendation Approach Uing Weighted Sequential Pattern and Markov Model Reference référence referencia April Global Journal of Computer Science and Technology Volume XII Iue IX Verion I Fig.3: Memory uage Fig.4 : Number of pattern mined The figure 2 how that the execution time of the W-prefixSpan algorithm i.5 Sec that i le compared with the time taken of the PrefixSpan algorithm. In memory uage, the W-prefixSpan algorithm needed only le than 50% memory compared with Prefixpan algorithm. Figure 4 how that number of pattern mined uing W-Prefixpan i high compared to Prefixpan algorithm. V. Concluion We have propoed a web page recommendation algorithm uing weighted equential pattern and markov model. Here, we have preented W-PrefixSpan algorithm that i developed by incorporating the weightage contraint uch a, pending time and recent viiting with the prefixpan algorithm. The mined weighted equential pattern are then utilized to contruct the recommendation model uing the patricia trie-baed tree tructure. At lat, markov model-baed recommendation i carried out for the current uer by matching the viiting path with the tree and markov model. The experimentation i done with the help of ynthetic dataet and the performance of W-Prefixpan algorithm a well a web page recommendation algorithm i analyzed. From the reult, the memory required for the W-prefixSpan algorithm i le than 50% of memory needed for PrefixSpan algorithm.. Baoyao Zhou, Siu Cheung Hui, Kuiyu Chang, "An Intelligent Recommender Sytem uing Sequential Web Acce Pattern", in proceeding of IEEE Conference on Cybernetic and Intelligent Sytem, Daniel Mican, Nicolae Tomai, "Aociation-Rule- Baed Recommender Sytem for Peronalization in Adaptive Web-Baed Application", Proceeding ICWE'0 Proceeding of the 0th international conference on Current trend in web engineering, pp.85-90, Forati, R., Meybodi, M.R., Rahbar, A. "An efficient algorithm for web recommendation ytem", IEEE/ACS International Conference on Computer Sytem and Application, pp , Faten Khalil, Jiuyong Li, Hua Wang, "Integrating Recommendation Model for Improved Web Page Prediction Accuracy", in Proceeding of the thirtyfirt Autralaian conference on Computer cience, Vol. 74, Hirohi Ihikawa, Manabu Ohta, Shohei Yokoyama, Junya Nakayama, Kaoru Katayama, "On the Effectivene of Web Uage Mining for Page Recommendation and Retructuring, Proceeding Revied Paper from the NODe 2002 Web and Databae-Related Workhop on Web, Web- Service, and Databae Sytem, pp , Jatin D Parmar and Sanjay Garg, "Modified Web Acce Pattern (mwap) Approach for Sequential Pattern Mining", Journal of computer Science, Vol. 6, No.2, pp.46-54, June Jian Pei, Jiawei Han, Mortazavi-Al B., Pinto H., Qiming Chen, Dayal U., Mei-Chun Hu, "PrefixSpan,: mining equential pattern efficiently by prefix-projected pattern growth", in Proceeding of 7th International Conference on Data Engineering, Qiankun Zhao and Sourav S. Bhowmick, "Sequential Pattern Mining: A Survey", Technical Report, CAIS, Nanyang Technological Univerity, Singapore, No.8, Sizu Hou, Xianfei Zhang, "Alarm Aociation Rule Baed on Sequential Pattern Mining Algorithm", In proceeding of the Fifth International Conference on Fuzzy Sytem and Knowledge Dicovery, vol. 2, pp , Shandong, Salvatore Orlando, Raffaele Perego and Claudio Silvetri, "A new algorithm for gap contrained equence mining", In Proceeding of the ACM Sympoium on Applied Computing, pp , Nicoia, Cypru, C.P. Sumathi,R. Padmaja Valli,T. Santhanam, "Automatic Recommendation of Web Page in Web 202 Global Journal Inc. (US)

8 Web Page Recommendation Approach Uing Weighted Sequential Pattern and Markov Model Uage Mining", (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 09,pp , Sarabjot Singh Anand, Bamhad Mobaher, "Intelligent Technique for Web Peronalization", ITWP 2003, LNAI 369, pp. -36, Springer-Verlag Berlin Heidelberg, Utpala Niranjan, R. B. V. Subramanyam and V. Khanaa, "Developing a Web Recommendation Sytem Baed on Cloed Sequential Pattern", Communication in Computer and Information Science, Vol. 0, no., pp. 7-79, Utpala Niranjan, R.B.V. Subramanyam, V.Khana, "An Efficient Sytem Baed On Cloed Sequential Pattern for Web Recommendation", IJCSI International Journal of Computer Science Iue, Vol. 7, Iue 3, No 4, May Yicen Liu, Mingrong Liu, Xing Chen, Liang Xiang, Qing Yang "Automatic Tag Recommendation for Weblog", International Conference on Information Technology and Computer Science, pp , April Global Journal of Computer Science and Technology Volume XII Iue IX Verion I 202 Global Journal Inc. (US)

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