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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Shrenik R Patil, , , ,
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 :,

Keywords : Recommender Systems, Machine Learning techniques, Hybridization Methods

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