An Efficient Method for Dynamic Recommendation

Abstract

Most of the traditional collaborative filtering recommendation algorithms do not take into account the factor the time the users evaluate the items, they calculate the similarity between the users only using the static data. In many real world applications, the user's interest may change with the time. In this paper, we present an efficient method for such dynamic recommendation. The method calculates the similarity between the users based on their evaluation scores and times on the items. A fading factor is defined to emphasis of the recent ratings. The experimental results show that the accuracy of the recommendation results by our method (UBCFT) is improved compared with the existing collaborative filtering algorithms.