HYBRIDIZATION OF WEB PAGE RECOMMENDER SYSTEMS BASED ON ML TECHNIQUES
Shrenik R Patil, , , ,
Affiliations Information Technology Department, D.K.T.E.’s Textile and Engineering Institute Ichalkaranji India
: World Wide Web is the biggest source of information. Though the World Wide Web contains a tremendous amount of data, most of the data is irrelevant and inaccurate from users’ point of view. Consequently, it has become increasingly necessary for users to utilize automated tools such as recommender systems in order to discover, extract, filter, and evaluate the desired information and resources. recommender systems (RS) are widely used in e-commerce, social networks and several other domains. Web page recommender systems predict the information needs of users and provide them with recommendations to facilitate their navigation. Web content and Web usage mining techniques are employed as conventional methods for recommendation. Machine Learning techniques used for recommender system are Clustering, Association rules and Markov models. These techniques have strengths and weaknesses. Combining different systems to overcome disadvantages and limitations of a single system may improve the performance of recommenders. Hybrid recommender systems can be used to avoid the drawbacks or limitations of previous recommendation method. They combine two or more methods to improve recommender performance. In this paper, the four recommender systems are combined by using different hybridization methods. The effects of the hybrid recommenders are examined by comparing the results of hybrid system against the results of single recommendation method. Result shows that the hybrid recommender provides successful recommendation when the recommended page is generated by all the systems of the hybrid.
Shrenik R Patil."HYBRIDIZATION OF WEB PAGE RECOMMENDER SYSTEMS BASED ON ML TECHNIQUES ". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.6, Issue 05,pp.310-316, May - 2019, URL :https://ijcert.org/ems/ijcert_papers/V6I501.pdf,
 Agrawal R., & Srikant R., "Fast algorithms for mining association rules," in J. B. Bocca, M. Jarke, & C. Zaniolo (Eds.), Proceedings of the 20th international conference on extensive databases, VLDB, 1994, pp. 487–499.
 Agrawal R., Swami A, Imieliński T., "Mining association rules between sets of items in large databases", Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD '93, 1993, p. 207.
 Barragáns-Martínez A. B., Costa-Montenegro E., Burguillo J. C., Rey-López, M., Mikic-Fonte, F. A., & Peleteiro, A. “A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition.” Information Sciences, 180(22), 2010 p. 4290–4311.
 Burke R. “Hybrid recommender systems: Survey and experiments”. User Modeling and User-Adapted Interaction, 12(4), 2007, 331–370.
 Deshpande M., & Karypis G., “Selective Markov models for predicting Web page accesses,” ACM Transactions on Internet Technology (TOIT), 4(2), 2004, 163–184.
 Ericson, K., & Pallickara, S. (2011, December). On the performance of distributed clustering algorithms in file and streaming processing systems. In Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on (pp. 33-40). IEEE.
 Ericson, K., & Pallickara, S. (2013). On the performance of high dimensional data clustering and classification algorithms. Future Generation Computer Systems, 29(4), 1024-1034.
 Gündüz S. & Özsu M. T., "A Web page prediction model based on Click-Stream Tree representation of user behavior", in Proceedings of 9th ACM international conference on knowledge discovery and data mining (KDD), Washington, DC, USA, August 2003.
 Magdalini Eirinaki and Michalis Vazirgiannis, “Web Mining for Web Personalization” Communications of the ACM, vol. 3, No. 1, Feb. 2003 pp.2-21.
 Mobasher B., Dai H., Luo T., & Nakagawa M., “Effective personalization based on association rule discovery from Web usage data” in Web information and data management, 2001, pp. 9–15.
 Tao Luo, Bamshad Mobasher, Honghua Dai, Miki Nakagawa, “Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization,”Data Mining and Knowledge Discovery6(1), 2002,p.61–82.
 Vlado Kesˇelj, Haibin Liu “Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users’ future requests”. Data & Knowledge Engineering 61, 2007, 304–330.
 Wang Q., Makarov D. J., and Edwards H. K., "Characterizing customer groups for an e-commerce website," EC'04, USA, 2004, p. 218-227.
 Lu, J., Shambour Q., Xu Y., Lin Q., & Zhang, G., “A Web-Based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques. Computational Intelligence,” in Press, 2012.
 Lucas, J. P., Segrera, S., & Moreno, M. N. (2012). Making use of associative classifiers to alleviate typical drawbacks in recommender systems. Expert Systems with Applications, 39(1), 1273-1283.
 Uyar A. S., Demir G. N., Goksedef M., "Effects of session representation models on the performance of web recommender systems," In Proceedings of the workshop on data mining and business intelligence, 2007, pp. 931–936.