Personalized web page recommendation using case-based clustering and weighted association rule mining

Recommendation system predicts and suggests those web pages that are likely to be visited by web users. The usage of recommendation system reduces delay in search and helps users to achieve the desired purpose in web search. Personalization in recommender system creates user profiles by analyzing the user's interest through previous search history and patterns. The web pages that are recommended will be predicted based on the user profile. In this paper, the idea of Case-Based Reasoning has been adapted suitable for web page recommendation as an extension of Collaborative filtering. Users' profile will be generated comprising of eight characteristic features and two content-based features generated using web access search logs. The collaboration among the k-NN user profile is identified based on Case-Based Reasoning. To enhance the accuracy Weighted Association Rule Mining is applied, which generates rules among the user profiles and optimally predicts the web pages suitable for the given search keyword by a user. To verify the effectiveness of the proposed idea, experiments were carried out with multiple datasets covering 2370 web pages accessed by 77 different users. Experiment result shows that the proposed algorithm outperforms existing methods with increased accuracy and minimum miss-out and fall-out rates.