Collaborative filtering recommendation based on dynamic changes of user interest

Collaborative filtering is probably the most familiar and most widely implemented recommendation algorithm. However, traditional collaborative filtering methods focus only on rating data to generate recommendation; they do not consider useful information like item genre and evaluation time, which a ffect the quality of the system's recommendation seriously. In similarity computation, traditional algorithms use all items; they do not introduce genre component in correlation between user and item. Furthermore, they do not consider the influence of time on user's interests; giving the same treatment to user's score at different time. To address this issue, a new item-based collaborative filtering algorithm is proposed to exploit genre information in each item and reflect dynamic changes over time of user's preferences. The proposed algorithm endows each score with a weight function which keeps user's recent, long and periodic interest, and attenuate user's old short interest. Experimental results from Movielens data set show that the new algorithm outperforms the traditional item-based collaborative filtering algorithms.