Neighborhood-Enhanced and Time-Aware Model for Session-based Recommendation

no code implementations • 25 Sep 2019 • Yang Lv, Liangsheng Zhuang, Pengyu Luo

Session based recommendation has become one of the research hotpots in the field of recommendation systems due to its highly practical value. Previous deep learning methods mostly focus on the sequential characteristics within the current session, and neglect the context similarity and temporal similarity between sessions which contain abundant collaborative information. In this paper, we propose a novel neural networks framework, namely Neighborhood Enhanced and Time Aware Recommendation Machine(NETA) for session based recommendation.

Session-Based Recommendations