HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation

TL;DR

HieRec introduces a hierarchical user interest modeling approach for personalized news recommendation, capturing diverse interests at multiple levels to improve recommendation accuracy.

Contribution

The paper proposes a hierarchical interest tree model and matching framework, enabling multi-grained user interest representation beyond single embeddings.

Findings

Significantly outperforms baseline methods on real-world datasets.

Effectively captures multi-grained user interests for better recommendations.

Improves user interest modeling accuracy in news recommendation systems.

Abstract

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.