CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation

• URL: http://arxiv.org/abs/2310.09401v6
• Date: Tue, 11 Feb 2025 06:26:34 GMT
• Title: CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation
• Authors: Yunyong Ko, Seongeun Ryu, Sang-Wook Kim,
• Abstract summary: We propose a novel personalized news recommendation framework (CROWN) that employs category-guided intent disentanglement.<n>CROWN provides consistent performance improvements over ten state-of-the-art news recommendation methods.
• Score: 17.100667350463464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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