Neural News Recommendation with Long- and Short-term User Representations

By Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie. Annual Meeting of the Association for Computational Linguistics, 2019

326 citations recorded by the source corpus.

Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both long-term preferences and short-term interests. However, existing news recommendation methods usually learn single representations of users, which may be insufficient. In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations. The core of our approach is a news encoder and a user encoder. In the news encoder, we learn representations of news from their titles and topic categories, and use attention network to select important words. In the user encoder, we propose to learn long-term user representations from the embeddings of their IDs.In addition, we propose to learn short-term user representations from their recently browsed news via GRU network. Besides, we propose two methods to combine long-term and short-term user representations. The first one is using the long-term user representation to initialize the hidden state of the GRU network in short-term user representation. The second one is concatenating both long- and short-term user representations as a unified user vector. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of neural news recommendation.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/b7de4958c47666ccc064e354e37b9056c96ea8c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fangzhao Wu
2. Chuhan Wu
3. Xing Xie
4. Yongfeng Huang
5. Tao Qi
6. M. Zhou
7. Jianfeng Gao
8. Mingxiao An

In Computer Vision and Pattern Recognition

1. M. Zhou
2. Xing Xie
3. Jianfeng Gao
4. Fangzhao Wu
5. Chuhan Wu
6. Yongfeng Huang
7. Jianxun Lian
8. Tao Qi

In Information Systems

1. Fangzhao Wu
2. Chuhan Wu
3. Yongfeng Huang
4. Tao Qi
5. Xing Xie
6. Mingxiao An
7. Jianqiang Huang
8. Jianxun Lian

Citations in the corpus

• When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “For example, NPA [96] and LSTUR [97] incorporate attention mechanisms to determine the importance of words after CNN layers.”
• FedRec: Privacy-Preserving News Recommendation with Federated Learning (2020, methodology): “Following many previous works (Okura et al., 2017; An et al., 2019), the news recommendation”
• NPA: Neural News Recommendation with Personalized Attention (2019, background): “Therefore, it is an important task for online news platforms to target user interests and make personalized news recommendation [1, 8, 14], which can help users to find their interested news articles and alleviate information overload [32, 35].”
• Neural News Recommendation with Multi-Head Self-Attention (2019, unknown): “Wu et al., 2019b,c,a; Zhu et al., 2019; An et al., 2019).”
• MIND: A Large-scale Dataset for News Recommendation (2020, methodology): “…( Attention ), candidate-aware attention used in (Wang et al., 2018) ( Candidate-Att ), gated recurrent unit used in (Okura et al., 2017) ( GRU ), long-and short-term user representation used in (An et al., 2019) ( LSTUR ) and multi-head self-attention used in (Wu et al., 2019c) ( Self-Att ).”