Improved hybrid information filtering based on limited time window

Adopting the entire collecting information of users, the hybrid information filtering of heat conduction and mass diffusion (HHM) (Zhou et al., 2010) was successfully proposed to solve the apparent diversity–accuracy dilemma. Since the recent behaviors are more effective to capture the users’ potential interests, we present an improved hybrid information filtering of adopting the partial recent information. We expand the time window to generate a series of training sets, each of which is treated as known information to predict the future links proven by the testing set. The experimental results on one benchmark dataset Netflix indicate that by only using approximately 31% recent rating records, the accuracy could be improved by an average of 4.22% and the diversity could be improved by 13.74%. In addition, the performance on the dataset MovieLens could be preserved by considering approximately 60% recent records. Furthermore, we find that the improved algorithm is effective to solve the cold-start problem. This work could improve the information filtering performance and shorten the computational time.