Using Temporal Information to Improve Predictive Accuracy of Collaborative Filtering Algorithms

In recent years, collaborative filtering becomes one of the most successful recommender systems. Its key technique is to predict new ratings from the known ratings. Unfortunately, in the previous research, the temporal information was rarely applied. That is to say, the ratings at different time were considered the same. However, from our point of view, not only the mean values of ratings in different periods are different, but users´ opinions toward items may change with the passage of time as well. We analyze the influence of the temporal information and introduce three methods to apply the temporal information. Firstly, by mixing user, item and time attributes, we present a regression-based method. Secondly, to guarantee that the ratings in different time can contribute different weights to the predicting rating, we adjust the prediction function by adding a parameter, which is a function of the time between the predicting rating and the known rating. Thirdly, we select different methods to predict ratings of different periods. Experiments on two real large datasets show that our methods are effective and can improve the accuracy.