CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation
It addresses the problem of information overload for users by improving news recommendation accuracy, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles challenges in personalized news recommendation, including comprehending manifold intents, differentiating post-read preferences, and addressing cold-start users, by proposing the CROWN framework, which shows consistent performance improvements over ten state-of-the-art methods in experiments on two real-world datasets.
Personalized news recommendation aims to assist users in finding news articles that align with their interests, which plays a pivotal role in mitigating users' information overload problem. Although many recent works have been studied for better personalized news recommendation, the following challenges should be explored more: (C1) Comprehending manifold intents coupled within a news article, (C2) Differentiating varying post-read preferences of news articles, and (C3) Addressing the cold-start user problem. To tackle the aforementioned challenges together, in this paper, we propose a novel personalized news recommendation framework (CROWN) that employs (1) category-guided intent disentanglement for (C1), (2) consistency-based news representation for (C2), and (3) GNN-enhanced hybrid user representation for (C3). Furthermore, we incorporate a category prediction into the training process of CROWN as an auxiliary task, which provides supplementary supervisory signals to enhance intent disentanglement. Extensive experiments on two real-world datasets reveal that (1) CROWN provides consistent performance improvements over ten state-of-the-art news recommendation methods and (2) the proposed strategies significantly improve the accuracy of CROWN.