HCURec: Hierarchical candidate-aware user modeling for news recommendation

Matching candidate news with user interests is critical for news recommendation. Current studies on news recommendation mainly model a single user interest embedding from the user's clicked news. One of the major challenges that affects the recommendation is to match candidate news with the user's multi-field and multi-grained interests. To confront this difficulty, we investigate the multi-grained correlation of user interests and candidate news to obtain their matching features. We propose a hierarchical candidate-aware user modeling framework for news recommendation that matches users' multi-field and multi-grained interests with candidate news. The framework incorporates candidate news into the modeling of user interests at different levels, namely subcategory-level, category-level and global-level, which learn fine-grained, coarse-grained and overall user interests, respectively. The user interest is finally hierarchically matched at different levels with candidate news to achieve accurate targeting. A collection of experiments were carried out on four real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the existing state-of-the-art methods owing to its highly effective and efficient performance of news recommendation.