Enhancing Collaborative Filtering Based Recommender System by Using Adaptive Clustering

Abstract -A recommender system is an information system that suggests items, web pages to a web user. Collaborative filtering based recommender system recommends the list of web pages to the user based on other similar user's preferences. Newly created web pages arriving to the search engine will not be considered for recommendation, as it is not visited by any users. This is termed as cold start problem. This work includes semantic similarity based relationship as an add-on to the frequency of keywords in the visited web pages. The work also uses adaptive clustering mechanism in order to cluster all the frequent keywords based on their relationship. When the user enters a query, the web pages that contain the keywords that are semantically similar to the top keywords in the matching cluster corresponding to query will be recommended to the user. The current recommender system considers not only the popular web pages, but also every newly created and dynamically modified web pages. Thus avoiding cold start problem and popularity bias. The system has been tested by providing single-keyword queries and the results are compared with existing collaborative based recommender systems. It has been observed that, the accuracy of the Semantic similarity based adaptive clustering recommendation technique has been increased by 20% comparing to existing system. Also the recommendation diversity has been increased by 11% to that of existing system