Neural News Recommendation with Attentive Multi-View Learning

By Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie. International Joint Conference on Artificial Intelligence, 2019

337 citations recorded by the source corpus.

Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods usually learn these representations based on single news information, e.g., title, which may be insufficient. In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information. The core of our approach is a news encoder and a user encoder. In the news encoder we propose an attentive multi-view learning model to learn unified news representations from titles, bodies and topic categories by regarding them as different views of news. In addition, we apply both word-level and view-level attention mechanism to news encoder to select important words and views for learning informative news representations. In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of news recommendation.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/e08716f95a8a3b8644bcc395493332c215af4384

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. Xiangnan He
2. Xiang Wang
3. Xing Xie
4. Fangzhao Wu
5. Chuhan Wu
6. Meng Wang
7. Yongfeng Huang
8. Tat-seng Chua

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Xing Xie
4. Fangzhao Wu
5. Chuhan Wu
6. Meng Wang
7. Le Wu
8. Yongfeng Huang

Citations in the corpus

• Neural News Recommendation with Multi-Head Self-Attention (2019, background): “Following (Wu et al., 2019b), the negative sampling ratio K is 4.”
• FedRec: Privacy-Preserving News Recommendation with Federated Learning (2020, methodology): “…attentive multi-view learning; (7) NPA (Wu et al., 2019b), using personalized attention network to learn news and user representations; (8) NRMS (Wu et al., 2019d), learning representations of news and users via multi-head self-attention network; (9) FCF (Ammad et al., 2019), a federated…”
• NPA: Neural News Recommendation with Personalized Attention (2019, background): “However, massive news articles are emerged everyday, and it is impractical for users to seek for interested news from a huge volume of online news articles [26, 34].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “, title, body, keywords and so on), multi-view attention networks are applied to learn unified item representations by aggregatingmultiple representations from different views [104], [105], [106].”
• MIND: A Large-scale Dataset for News Recommendation (2020, methodology): “First, news is a common form of texts, and text modeling et al., 2019; Wu et al., 2019c).”