Graph Neural News Recommendation with Long-term and Short-term Interest Modeling
This addresses the problem of helping users find relevant news more efficiently in information-rich environments, though it is incremental as it builds on existing graph-based and sequence modeling techniques.
The paper tackles the data sparsity problem in personalized news recommendation by building a heterogeneous graph with users, news, and latent topics, using graph neural networks to capture high-order structure and long-term interests, and an attention-based LSTM for short-term interests, achieving significant performance improvements over state-of-the-art methods on real-world datasets.
With the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history. Although these methods have achieved good performances, they still suffer from data sparse problem, since most of them fail to extensively exploit high-order structure information (similar users tend to read similar news articles) in news recommendation systems. In this paper, we propose to build a heterogeneous graph to explicitly model the interactions among users, news and latent topics. The incorporated topic information would help indicate a user's interest and alleviate the sparsity of user-item interactions. Then we take advantage of graph neural networks to learn user and news representations that encode high-order structure information by propagating embeddings over the graph. The learned user embeddings with complete historic user clicks capture the users' long-term interests. We also consider a user's short-term interest using the recent reading history with an attention based LSTM model. Experimental results on real-world datasets show that our proposed model significantly outperforms state-of-the-art methods on news recommendation.