HYBRIDIZATION OF WEB PAGE RECOMMENDER SYSTEMS BASED ON ML TECHNIQUES
Main Article Content
Abstract
World Wide Web is the most significant 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 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 the recommendation. Machine Learning techniques used for recommender system are Clustering, Association rules, and Markov models. These techniques have strengths and weaknesses. Combining different methods to overcome the 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 a hybrid system against the results of a single recommendation method. The result shows that the hybrid recommender provides strong recommendation when all the systems of the hybrid generate the recommended page.
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