- Title
- LSPM : joint deep modeling of long-term preference and short-term preference for recommendation
- Creator
- Chen, Jie; Jiang, Lifen; Sun, Huazhi; Ma, Chunmei; Liu, Zekang; Zhao, Dake
- Date
- 2019
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/173726
- Identifier
- vital:14709
- Identifier
-
https://doi.org/10.1007/978-3-030-36808-1_26
- Identifier
- ISBN:1865-0929 (ISSN); 9783030368074 (ISBN)
- 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/. © Springer Nature Switzerland AG 2019.
- Publisher
- Springer
- Relation
- 26th International Conference on Neural Information Processing, ICONIP 2019 Vol. 1142 CCIS, p. 237-246
- Rights
- Copyright © 2020 Springer Nature Switzerland AG. Part of Springer Nature.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Collaborative filtering; Deep learning; Long-term preference; Short-term preference
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