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/.
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Acknowledgment
This work was supported in part by National Natural Science Foundation of China (NO. 61702370); Natural Science Foundation of Tianjin (NO. 17JCYBJC16 400, 18JCQNJC70200, 18JCYBJC85900); Research project of Tianjin science and technology development strategy (NO. 17ZLZXZF00530); 131 three-level candidates of Tianjin Normal University (NO. 043/135305QS20); Doctoral Fund of Tianjin Normal University (NO. 043/135202XB1615, NO. 043/135202XB1705).
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Chen, J., Jiang, L., Sun, H., Ma, C., Liu, Z., Zhao, D. (2019). LSPM: Joint Deep Modeling of Long-Term Preference and Short-Term Preference for Recommendation. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_26
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DOI: https://doi.org/10.1007/978-3-030-36808-1_26
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