Temporal information effect on personalized recommendation

Abstract

As information explosively grows, it requires to find out the highly efficient data from the global database. In this paper, based on two real systems, i.e., the MovieLens and Netflix, we investigate the temporal information effect on personalized recommendation for three collaborative filtering algorithms, by considering both the volume and time of the training data. It is observed that, for an increasing training data of a certain time range, the recommendation accuracy using the time-dependent training data outperforms that using the random training data of the same size. Moreover, the increase of data volume would not always improve the recommendation accuracy for the time-dependent training data case, whereas the recent temporal training data are important to elevate the recommendation accuracy. Further study on a hybrid algorithm of heat conduction and mass diffusion shows that, using the recent temporal information as training data, the algorithm reaches the optimal recommendation accuracy, when it returns to the original mass diffusion algorithm, which suggests no necessity of introducing an extra parameter. And the simple mass diffusion algorithm is found to be more advantageous than the previous three algorithms.