A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommendation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on association rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the frequency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages that are not yet visited by users is not included in the recommendation set. To overcome this problem, we have used the web usage log in the adaptive association rule based web mining where the association rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.

[1]  Mathias Géry,et al.  Evaluation of web usage mining approaches for user's next request prediction , 2003, WIDM '03.

[2]  Salvatore Orlando,et al.  A new algorithm for gap constrained sequence mining , 2004, SAC '04.

[3]  Osmar R. Zaïane,et al.  Mission-Based Navigational Behaviour Modeling for Web Recommender Systems , 2004, WebKDD.

[4]  Domonkos Tikk,et al.  Scalable Collaborative Filtering Approaches for Large Recommender Systems , 2009, J. Mach. Learn. Res..

[5]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[6]  Tao Luo,et al.  Effective personalization based on association rule discovery from web usage data , 2001, WIDM '01.

[7]  Cheng-Zen Yang,et al.  Cross-Lingual News Group Recommendation Using Cluster-Based Cross-Training , 2008, Int. J. Comput. Linguistics Chin. Lang. Process..

[8]  Cyrus Shahabi,et al.  Knowledge discovery from users Web-page navigation , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

[9]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[10]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[11]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[12]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[13]  Mohammad Reza Meybodi,et al.  An efficient algorithm for web recommendation systems , 2009, 2009 IEEE/ACS International Conference on Computer Systems and Applications.

[14]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[15]  Bamshad Mobasher,et al.  Web Usage Mining and Personalization , 2004, The Practical Handbook of Internet Computing.

[16]  K. Fernow.  New York , 1896, American Potato Journal.

[17]  Linas Baltrunas,et al.  Towards Time-Dependant Recommendation based on Implicit Feedback , 2009 .

[18]  Varsha Negi.  Recommender System , 2013 .

[19]  Eyke Hüllermeier,et al.  A systematic approach to the assessment of fuzzy association rules , 2006, Data Mining and Knowledge Discovery.