In the era of information explosion, recommender systems have become an indispensable part of modern life. Users' decisions usually are determined by long-term and short-term preferences together. However, most of the existing work usually study these two requirements separately. In this paper, we attempt to build the bridge between long-term preference and short-term preference. 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 long-term preference by a neural latent factor model from historical interactions. We conduct experiments to demonstrate the effectiveness of LSPM on three public datasets. Compared with the state-of-the-art methods, LSPM gets a massive improvement in HR@10 and NDCG@10, which relatively increased by %3.875 and %6.363.
$ git clone https://github.com/chenjie04/LSPM.git
$ cd LSPM/
$ sudo pip3 install -r requirements.txt
$ cd data/
$ bash download_dataset.sh
$ python3 main.py
If this work is useful for your research, please cite our paper:
@inproceedings{chen2019lspm,
title={LSPM: Joint Deep Modeling of Long-Term Preference and Short-Term Preference for Recommendation},
author={Chen, Jie and Jiang, Lifen and Sun, Huazhi and Ma, Chunmei and Liu, Zekang and Zhao, Dake},
booktitle={International Conference on Neural Information Processing},
pages={237--246},
year={2019},
organization={Springer}
}