Personalized News Recommendation with Knowledge-aware Interactive Matching
This work addresses the challenge of enhancing recommendation accuracy for users in news platforms, representing an incremental improvement over existing methods.
The paper tackles the problem of inaccurate matching between candidate news and user interests in personalized news recommendation by proposing a knowledge-aware interactive matching method that interactively models both candidate news and user interests, resulting in improved performance on two real-world datasets.
The most important task in personalized news recommendation is accurate matching between candidate news and user interest. Most of existing news recommendation methods model candidate news from its textual content and user interest from their clicked news in an independent way. However, a news article may cover multiple aspects and entities, and a user usually has different kinds of interest. Independent modeling of candidate news and user interest may lead to inferior matching between news and users. In this paper, we propose a knowledge-aware interactive matching method for news recommendation. Our method interactively models candidate news and user interest to facilitate their accurate matching. We design a knowledge-aware news co-encoder to interactively learn representations for both clicked news and candidate news by capturing their relatedness in both semantic and entities with the help of knowledge graphs. We also design a user-news co-encoder to learn candidate news-aware user interest representation and user-aware candidate news representation for better interest matching. Experiments on two real-world datasets validate that our method can effectively improve the performance of news recommendation.