Exploring user movie interest space: A deep learning based dynamic recommendation model

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 ratingbased 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.

Automated summary

This study explores user interest characteristics and provides dynamic movie recommendations by constructing UMIS model and proposing DIF deep learning model. Experimental results show that DIF model outperforms traditional models and other state-of-the-art deep learning models in predicting future user interests in a multi-dimensional user interest space.