Exploring user movie interest space: A deep learning based dynamic recommendation model. (1st July 2021) - Gan, Mingxin; Cui, Hongfei
Highlights: A sequence-based deep learning model for movie recommendation is proposed. User Movie Interest Space (UMIS) is introduce to reflect user similarities. Three indexes are developed to describe UMIS from multiple points of interest. Rating sequence predictions based on UMIS perform better than those without UMIS. The method greatly improves dynamic recommendation performance than other methods. Abstract: Exploring user interest behind massive user behaviors is essential for online recommendations. Although recommendation models have been proposed recently with great success, existing studies ignore not only the timeliness of online users' behaviors in terms of their interest, but also the sequential characteristics of their behaviors. To overcome this limitation, we construct a User Movie Interest Space (UMIS) model based on the sequential ratings of users. We define three indexes to elucidate the features of the interest of users for UMIS, which describe different patterns of behaviors of users related to their interests. Based on UMIS we propose a deep learning model named Dynamic Interest Flow (DIF) to provide dynamic movie recommendations. The DIF model achieves intelligently multi-dimensional observations on a user's interest space and to predict simultaneously a variety of their future interests. Experimental results indicate that DIF outperforms traditional rating-based models and other state-of-the-art deep learning models. Results also demonstrate that modeling a dynamic recommendation as a sequential prediction is supposed to obtain outstanding advantages.