Dynamic item feature modeling for rating prediction in recommender systems

Xianglin Zuo, Shining Liang, Xiaosong Yuan, Shuang Yu, Bo Yang

Abstract: Highlights•We propose HCoTemp to model both static and dynamic item feature representations.•The model can predict both user preference evolution and hot item evolution.•Experiments on Amazon datasets show HCoTemp outperform state-of-the-art models.