Incorporating temporal dynamics into LDA for one-class collaborative filtering

In the one-class collaborative filtering (OCCF) scenario, the elements of the user-item rating matrix consist each take one of only two values: either “like” or unknown. Previous methods for solving the OCCF problem can be roughly categorized into content-based methods, pointwise methods, and pairwise methods. A fundamental assumption of these approaches is that all missing values in a rating matrix can be treated as “dislike”. However, this assumption may not hold because the missing values are not always negative. Sometimes users do not give positive feedback on an item simply because they are not familiar with it rather than because they dislike it. In addition, content-based methods usually require textual information on the items. In many cases, however, sufficient textual information is not available; therefore, content-based methods are not applicable. Moreover, a user's preference for items usually drifts over time, but the previous methods cannot capture the temporal dynamics of this drift. In this paper, we propose to modify the latent Dirichlet allocation (LDA) model to address the above-mentioned problems. Our method uses only observed rating data to predict users' interests and effectively avoids the issue of data skew. Furthermore, to address the issue that users' preferences for items usually drift over time, we assign a different weight to each rating according to its timestamp when using Gibbs sampling to estimate the parameters of the LDA model. In this way, the temporal dynamics of the user preferences can be captured. We report experiments conducted to evaluate our model. The results show that the proposed model outperforms state-of-the-art approaches for the OCCF problem.