LSPM: Joint Deep Modeling of Long-Term Preference and Short-Term Preference for Recommendation

Jie Chen, Lifen Jiang, Huazhi Sun, Chunmei Ma, Zekang Liu, Dake Zhao

Abstract: In the era of information, recommender systems are playing an indispensable role in our lives. A lot of deep learning based recommender systems have been created and proven to be good progress. However, users’ decisions are determined by both long-term and short-term preferences, and most of the existing efforts study these two requirements separately. In this paper, we seek to build a bridge between the long-term and short-term preferences. We propose a Long & Short-term Preference Model (LSPM), which incorporates LSTM and self-attention mechanism to learn the short-term preference and jointly model the long-term preference by a neural latent factor model. We conduct experiments to demonstrate the effectiveness of LSPM on three public datasets. Compared with the state-of-the-art methods, LSPM got a significant improvement in HR@10 and NDCG@10, which relatively increased by $$3.875\%$$ and $$6.363\%$$. We publish our code at https://github.com/chenjie04/LSPM/ .