New hybrid web personalization framework

Web personalized recommender systems based on web mining try to mine users´ behavior patterns from web access logs and site metadata, and recommend pages to the online user by matching the user´s browsing behavior with the mined previous user´s behavior patterns. Recommendation approaches proposed in previous works, however, cannot still satisfy users especially in huge and dynamic web sites. To provide recommendation efficiently, we advance a framework for web mining-based personalization that combines web usage data with web content and site structure for predicting users´ future requests more accurately. The experimental results on real dataset show that the approach can improve accuracy and coverage of recommendations to users.