Deep Recommendation Model Combining Long - and Short-Term Interest Preferences

Abstract

The existing sequential recommendation algorithms cannot effectively capture and solve the problems such as the dynamic preferences of users over time. This paper proposes a deep Recommendation model CLSR (Combines Long-term and Short-term interest Recommendation) that Combines long-term and short-term interest preferences. Firstly, the model models the potential feature representation of users and items, and uses the self-attention mechanism to capture the relationship between items in the interaction of users’ historical behavior, so as to better learn the short-term interest representation of users. At the same time, the BiGRU network is used to extract the features of users’ long-term interests on a deep level. Finally, the features of long-term and short-term interest are fused. On four publicly available datasets, experimental results show that the proposed method has better improvement on HR@N, NDCG@N and MRR@N, which validates the effectiveness of the model.

Keywords

• Recommendation algorithm
• self-attention mechanism
• bidirectional gating cyclic network
• sequence recommendation
• deep learning