Recency-based collaborative filtering

Collaborative filtering is regarded as one of the most promising recommendation algorithms. Traditional approaches for collaborative filtering do not take concept drift into account. For example, user purchase interests may be volatile. A new mother may be interested in baby toys, although previously she had no interest in these. A man may like romantic films while he preferred action movies one year ago. Collaborative filtering is characterized by concept drift in the real world. To make time-critical predictions, we argue that the target users’ recent ratings reflect his/her future preferences more than older ratings. In this paper, we present a novel algorithm namely recencybased collaborative filtering to explore the weights for items based on their expected accuracy on the future preferences. Our proposed approach is based on itembased collaborative filtering algorithms. Specifically, we design a new similarity function to produce similarity scores that better reflect the reality. Our experimental results have shown that the new algorithm substantially improves the precision of traditional collaborative filtering algorithms.