A Hybrid News Recommendation Approach Based on Title-Content Matching

Abstract Personalized news recommendation can alleviate the information overload problem, and accurate modeling of user interests is the core of personalized news recommendation. Existing news recommendation methods integrate the titles and contents of news articles that users have historically browsed to construct user interest models. However, this method ignores the phenomenon of "title-content mismatching" in news articles, which leads to the lack of precision in user interest modeling. Therefore, a hybrid news recommendation method based on title-content matching is proposed in this paper(1)An interactive attention network is employed to model the correlation between title and content contexts, thereby enhancing the feature representation of both; (2) The degree of title-content matching is computed using a Siamese Neural Network, constructing a user interest model based on title-content matching; and (3) Neural Collaborative Filtering (NCF) based on Factorization Machines (FM) is integrated, taking into account the perspective of the potential relationships between users for recommendation, leveraging the insensitivity of neural collaboration to news content to alleviate the impact of title-content mismatching on user feature modeling. The proposed model was evaluated on a real dataset, and the experimental results demonstrate that the proposed method effectively improved the performance of news recommendation.