A New Recommender System Based on Multiple Parameters and Extended User Behavior Analysis

In this paper, a new recommender system has been developed based on extended user behavior analysis. The proposed system extracts information from recall, precision, number of clicked items, sequence of the clicked items, duration of tracking, number of tracking same item, like/unlike, association rules of clicked items, and remarks for items. The developed recommender system has been applied to a movie web site and experimental results have been obtained from undergraduate and graduate students at Gazi University, Ankara, Turkey. The proposed recommender system has been tested and compared extensively with the collaborative filtering. The experimental results show that the developed recommender system is more successful than collaborative filtering.

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