HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
This addresses the challenge of accurately capturing varied user interests for news recommendation, though it is incremental as it builds on existing methods with a hierarchical approach.
The paper tackles the problem of modeling diverse and multi-grained user interests in personalized news recommendation by proposing HieRec, which uses a hierarchical interest tree instead of a single user embedding, and shows it effectively improves performance in experiments on two real-world datasets.
User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest. However, user interest is usually diverse and multi-grained, which is difficult to be accurately modeled by a single user embedding. In this paper, we propose a news recommendation method with hierarchical user interest modeling, named HieRec. Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news. We use a three-level hierarchy to represent 1) overall user interest; 2) user interest in coarse-grained topics like sports; and 3) user interest in fine-grained topics like football. Moreover, we propose a hierarchical user interest matching framework to match candidate news with different levels of user interest for more accurate user interest targeting. Extensive experiments on two real-world datasets validate our method can effectively improve the performance of user modeling for personalized news recommendation.