NPA: Neural News Recommendation with Personalized Attention

Abstract

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.

References in corpus (4)

• DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
• Neural Collaborative Filtering
• DKN: Deep Knowledge-Aware Network for News Recommendation
• Neural News Recommendation with Attentive Multi-View Learning

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• Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
• USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
• NewsEmbed: Modeling News through Pre-trained Document Representations
• From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
• DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation
• HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
• Empowering News Recommendation with Pre-trained Language Models
• Social Explorative Attention based Recommendation for Content Distribution Platforms
• Research Progress of News Recommendation Methods
• Learning to Select Historical News Articles for Interaction based Neural News Recommendation
• Why Do We Click: Visual Impression-aware News Recommendation
• PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity
• Training Large-Scale News Recommenders with Pretrained Language Models in the Loop
• Personalized News Recommendation with Knowledge-aware Interactive Matching
• Neural News Recommendation with Negative Feedback
• Neural News Recommendation with Collaborative News Encoding and Structural User Encoding
• DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
• Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks