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

Ujwala H. Wanaskar, Sheetal R. Vij, Debajyoti Mukhopadhyay

Department of Computer Engineering, Maharashtra Institute of Technology, Pune, India.
Department of Computer Engineering, Padmabhooshan Vasantdata Patil Institute of Technology, Pune, India.
Department of Information Technology, Maharashtra Institute of Technology, Pune, India.

DOI: 10.4236/jsea.2013.68049                

Abstract

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.

Keywords

Web Recommender System; Association Rules; Web Mining; Text Mining

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	D. Dubois, E. Hüllermeier and H. Prade, “A Systematic Approach to the Assessment of Fuzzy Association Rules,” Data Mining and Knowledge Discovery Journal, Vol. 13, No. 2, 2006, pp. 167-192. doi:10.1007/s10618-005-0032-4
[2]	P. Resnik and H. Varian, “Recommender System,” Communication of the ACM, Vol. 40, No. 3, 1997.
[3]	G. Takacs, I. Pilaszy, B. Nemeth and D. Tikk, “Scalable Collaborative Filtering Approaches for Large Recommender Systems,” The Journal of Machine Learning Research, Vol. 10, 2009, pp. 623-656.
[4]	S. Orlando, R. Perego and C. Silvestri, “A New Algorithm for Gap Constrained Sequence Mining,” Proceedings of the ACM Symposium on Applied Computing, Nicosia, 14-17 March 2004, pp. 540-547,
[5]	R. Burke, “Hybrid Recommender System: Survey and Experiments,” User Modeling and User-Adapted Interaction, Vol. 12, No. 4, 2002, pp. 331-370
[6]	Baltrunas and X. Amatriain, “Towards Time-Dependant Recommendation Based on Implicit Feedback,” Workshop on Context-Aware Recommender Systems, New York, October 2009.
[7]	Y. Shavitt, W. Ela and W. Udi, “Building Recommender Syterm Using Peer to Peer Shared Content,” CIKM’10, Toronto, 25-29 October 2010.
[8]	C.-Z. Yang, I.-X. Chen and P.-J. Yu, “Cross Lingual News Group Recommendation Using Cluster Based cross Training,” Computational Linguistic and Chinese Language Processing, Vol. 13, No. 1, 2008, pp. 41-60.
[9]	B. Sarwar, G. Karypis, J. Konstan and J. Reidl, “ItemBased Collaborative Filtering Recommendation Algorithms,” In: Proceedings of the 10th International Conference on World Wide Web, ACM, New York, 2001, pp. 285-295.
[10]	H. Wang and S. M. Thao, “A Study on Personalized Web Browsing Recommendation Based on Data Mining and Collaborative Filtering Technology,” Proceedings of National Computer Symposium, Taiwan, 2003, pp. 18-25.
[11]	Gery and H. Haddad, “Evaluation of Web Usage Mining Approaches for User’s Next Request Prediction,” Proceedings of the Fifth ACM International Workshop on Web Information and Data Management, New Orleans, 7-8 November 2003, pp. 74-81.
[12]	B. Mobasher, R. Cooley and J. Srivastava, “Automatic Personalization Based on Web Usage Mining,” Communications of the ACM, Vol. 43, No. 8, 2000, pp. 142-151. doi:10.1145/345124.345169
[13]	B. Mobasher, H. Dai, T. Luo and M. Nakagawa, “Effective Personalization Based on Association Rule Discovery from Web Usage Data,” Proceedings of the 3rd ACM Workshop on Web Information and Data Management (WIDM01), Atlanta, 5-10 November 2001.
[14]	B. Mobasher, “Web Usage Mining and Personalization,” In: Practical Handbook of Internet.
[15]	C. Shahabi, A. Zarkesh, J. Abidi and V. Shah, “Knowledge Discovery from User’s Web-Page Navigation,” Proceedings of the 7th IEEE International Workshop on Research Issues in Data Engineering, Birmingham, 7-8 April 1997.
[16]	R. Forsati, M. Meybodi and A. Rahbar, “An Efficient Algorithm for Web Recommendation Systems,” AICCSA 2009 IEEE/ACS International Conference on Computer Systems and Applications, Rabat, 10-13 May 2009, pp. 579-586. doi:10.1109/AICCSA.2009.5069385
[17]	R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,” Proceedings of the 20th International Conference on Very Large Data Bases VLDB’94, Santiago, 12-15 September 1994, pp. 487-499.
[18]	J. M. Kleinberg, “Authoritative Sources in a Hyperlinked Environment,” Journal of the ACM, Vol. 46, No. 5, 1999, pp. 604-632. doi:10.1145/324133.324140
[19]	O. Za?ane, J. Li and R. Hayward, “Mission-Based Navigational Behavior Modeling for Web Recommender System,” Springer-Verlag, Berlin, Heidelberg, 2007.