A recommender system with interest-drifting

Collaborative filtering and content-based recommendation methods are two major approaches used in recommender systems. These two methods have some drawbacks in dealing with situations such as sparse data and cold start problems. Recently, combined methods were proposed to overcome these problems. However, a highly effective recommender system may still face a new challenge on interest drift. In this case, customer interests may change over time. For example, more recent users' ratings on items may reflect more on users' current interests than those of long time ago. Unfortunately, current available combination approaches do not consider this important factor and training data sets are regarded as static and time-insensitive. In this paper, we present a novel hybrid recommender system to overcome the interest drift problem by embedding the time-sensitive functions into the recommendation process. The users' interests changing behaviours are considered with time function. Our experiments demonstrate a better performance than that of the collaborative filtering approaches considering interests drift and those of the combined approaches without considering interests drift.