Web page recommender systems play a major role in web searches by retrieving most relevant results. The goal of personalized recommendation is to tailor the search results to a particular user based on his/her interest. Traditional retrieval systems are not adaptive enough to satisfy the user’s individual needs and interests. A collaborative filtering approach, called Normal Recovery Collaborative Filtering (NRCF) is used to increase the accuracy of webpage recommendation. As an enhancement, this work applies Case Based Reasoning (CBR) in web searches to optimize the retrieval strategy and Weighted Association Rule Mining (WARM) algorithm to predict more accurate webpages using association rules generated specifically for individual user profiles. For any active user, the system retrieves most similar user profiles matching the current user. Weighted rules are generated based on the frequency of visit and duration spent on the page. WARM is based on the profile similarity between the active user and the computed weighted rules. Based on these rules, new pages that visited by similar users are recommended to the active user. Experiment results show that the proposed algorithm combining CBR and WARM outperforms well with more accuracy by providing more efficient and appropriate recommendation.

Normal Recovery Collaborative Filtering, Case Based Reasoning (CBR), Weighted Association Rule Mining (WARM), Hypertext Induced Topic Search (HITS)

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