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The overload of information on the Internet becomes ubiquitous nowadays, which makes the role of recommender systems more important. In recommender systems, the interest of users and popularity of items are not static, but can change drastically. Thus modeling the temporal dynamic of user-item interactions is crucial in recommender systems. The newly proposed Neural Ordinary Differential Equation (NODE) method is able to modeling the temporal mechanism of a system with neural networks. By using the ODE-LSTM method, which unites the ability of NODE to handle continuous time and that of LSTM to address sequential data, in this paper we achieve significant improvements for the recommendation task on several real-world datasets with the time irregularity. To handle sessions with different timestamps in ODE-LSTM, we propose a collective timeline technique that contributes a lot to the performance improvement. Moreover, we find that reducing the scale of time intervals in sessions significantly improves the recommendation performance. |
