RecSys'25IP2: Entity-Guided Interest Probing for Personalized News Recommendation
Youlin Wu, Yuanyuan Sun, Xiaokun Zhang*, Haoxi Zhan, Bo Xu, Liang Yang, Hongfei Lin
Abstract
News recommender systems aim to provide personalized news reading experiences for users based on their reading history. Behavioral science studies suggest that screen-based news reading contains three successive steps: scanning, title reading, and then clicking. Adhering to these steps, we find that intra-news entity interest dominates the scanning stage, while the inter-news entity interest guides title reading and influences click decisions. Unfortunately, current methods overlook the unique utility of entities in news recommendation. To this end, we propose a novel method called IP2 to probe entity-guided reading interest at both intra- and inter-news levels. At the intra-news level, a Transformer-based entity encoder is devised to aggregate mentioned entities in the news title into one signature entity. Then, a signature entity-title contrastive pre-training is adopted to initialize entities with proper meanings using the news story context, which in the meantime facilitates us to probe for intra-news entity interest. As for the inter-news level, a dual tower user encoder is presented to capture inter-news reading interest from both the title meaning and entity sides. In addition to highlighting the contribution of inter-news entity guidance, a cross-tower attention link is adopted to calibrate title reading interest using inter-news entity interest, thus further aligning with real-world behavior. Extensive experiments on two real-world datasets demonstrate that our IP2 achieves state-of-the-art performance in news recommendation.
Cite our work!
@inproceedings{wu2025IP2,
title={IP2: Entity-Guided Interest Probing for Personalized News Recommendation},
author={Wu, Youlin and Sun, Yuanyuan and Zhang, Xiaokun and Zhan, Haoxi and Xu, Bo and Yang, Liang and Lin, Hongfei},
booktitle={Proceedings of the 19th ACM conference on recommender systems},
year={2025},
doi={10.1145/3705328.3748091}
}