News Recommendation with Candidate-aware User Modeling

TL;DR

This paper introduces a candidate-aware user modeling approach for news recommendation that incorporates candidate news into user interest modeling, improving matching accuracy and recommendation performance.

Contribution

It proposes novel candidate-aware neural networks for global interest, local context, and user representation modeling, enhancing personalization in news recommendation systems.

Findings

Significant improvement in recommendation accuracy on real-world datasets

Effective modeling of user interests tailored to candidate news

Enhanced matching between news articles and user preferences

Abstract

News recommendation aims to match news with personalized user interest. Existing methods for news recommendation usually model user interest from historical clicked news without the consideration of candidate news. However, each user usually has multiple interests, and it is difficult for these methods to accurately match a candidate news with a specific user interest. In this paper, we present a candidate-aware user modeling method for personalized news recommendation, which can incorporate candidate news into user modeling for better matching between candidate news and user interest. We propose a candidate-aware self-attention network that uses candidate news as clue to model candidate-aware global user interest. In addition, we propose a candidate-aware CNN network to incorporate candidate news into local behavior context modeling and learn candidate-aware short-term user interest. Besides, we use a candidate-aware attention network to aggregate previously clicked news weighted by their relevance with candidate news to build candidate-aware user representation. Experiments on real-world datasets show the effectiveness of our method in improving news recommendation performance.