An Incremental Collaborative Filtering based Recommendation Framework for Personalized Websites

To solve the problem that the user's retrieval intention is seldom considered in the personalized websites, we propose an improved incremental collaborative filtering (CF) based recommendation implementation method (ICFR) in this paper. The ICFR model uses collaborative filtering recommendation algorithm into the personalized websites. This paper firstly addresses the CF algorithm to obtain the relationship between user preference and recommendation content. Secondly, the browsing behavior information of users are extracted by analyzing web logs and then normalized into the rating value. Finally, the incremental algorithm is designed to update historical user preference data. Based on this established model, we propose some cases for this architecture, which illustrate that ICFR model is suitable for personalized websites in recommendation.