近日，RecSys 2025 公布了论文录用结果。实验室博士生吴有霖关于新闻推荐的研究被录用为长文。RecSys是信息检索与数据挖掘领域的旗舰会议，被CCF推荐为B类国际学术会议。这次会议共收到261篇长文投稿，其中有49篇论文被录用，录用率仅为18.8%。

IP2：面向个性化新闻推荐的实体导向兴趣探测

题目：IP2: Entity-Guided Interest Probing for Personalized News Recommendation

摘要：News recommender systems aim to deliver personalized news articles 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 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 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 in the news 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 title meaning and entity sides. In addition, to highlight 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.

中文摘要：新闻推荐旨在根据用户的阅读历史为他们提供个性化的阅读体验。行为科学研究表明，基于屏幕的在线新闻阅读包含三个连续的步骤：扫读、标题阅读以及点击。依照这些步骤，我们发现用户对新闻内部实体的兴趣在扫读阶段占主导地位，而跨越不同新闻间的实体兴趣指导了标题阅读过程并最终影响点击决策。不幸的是，当前的方法忽略了新闻推荐中实体的独特效用。为此，我们提出了一种名为IP2的新方法，以探测实体在新闻内和新闻间两个层次引导的阅读兴趣。在新闻内层次，我们设计了一种基于Transformer的实体编码器，用于将新闻标题中提及的实体聚合为一个签名实体。然后，采用签名实体-标题对比预训练来利用上下文信息对实体含义进行初始化，这同时也促成了对新闻内实体兴趣的探测。对于新闻间层次，我们提出了一种双塔用户编码器，以同时捕捉来自标题语义和实体表示两方面的阅读兴趣。此外，为了突出新闻间实体引导的贡献，采用了一种跨塔注意力连接来校准标题阅读偏好，从而进一步与现实世界的用户行为模式对齐。在两个真实世界数据集上的广泛实验表明，我们的IP2在新闻推荐中达到了领先的性能表现。