NPA: Neural News Recommendation with Personalized Attention

By Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie. Knowledge Discovery and Data Mining, 2019

367 citations recorded by the source corpus.

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/57eb5a5d584844e140ac51f17d5c3f5a24802daf

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. Cong Yu

In Information Systems

1. Fangzhao Wu
2. Chuhan Wu
3. Yongfeng Huang
4. Tao Qi
5. Xing Xie
6. Kun Zhang
7. Mingxiao An
8. Jianqiang 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.”
• 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).”
• 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…”
• Neural News Recommendation with Long- and Short-term User Representations (2019, background): “Wu et al. (2019) proposed to learn news and user representations with personalized word-and news-level attention networks, which exploits the embedding of user ID to generate the query vector for the attentions.”
• NewsEmbed: Modeling News through Pre-trained Document Representations (2021, methodology): “[58] applies a convolutional neural network (CNN) to encode news articles followed by a word-level personalized attention network.”
• Neural News Recommendation with Attentive Multi-View Learning (2019, background): “News recommendation is an important task in natural language processing and data mining fields, and has wide applications [ Zheng et al. , 2018; Wu et al. , 2019a ] .”