提出联合预测推荐时机与内容的新框架，提升主动推荐效果。
When and What to Recommend: Joint Modeling of Timing and Content for Active Sequential Recommendation
- 用扩散模型联合建模用户下次互动时间与感兴趣内容
- 在五个数据集上优于8个主流基线模型
- 适合需要主动推送的场景如电商、新闻推荐
序列推荐模型用于预测用户下一个目标项目。现有大多数工作是被动的，系统仅在用户打开应用时响应，错失关闭后的推荐机会。我们研究主动推荐，即预测下一次交互时间并主动推送项目。主要挑战在于准确估计兴趣时间（ToI）和根据预测的ToI生成感兴趣的项目（IoI）。我们提出PASRec，一种基于扩散的框架，通过联合目标将ToI与IoI对齐。在五个基准数据集上的实验表明，该方法在留一法和时间划分下均优于八个最先进的基线模型。
原文摘要 · Abstract (English)
Sequential recommendation models user preferences to predict the next target item. Most existing work is passive, where the system responds only when users open the application, missing chances after closure. We investigate active recommendation, which predicts the next interaction time and actively delivers items. Two challenges: accurately estimating the Time of Interest (ToI) and generating Item of Interest (IoI) conditioned on the predicted ToI. We propose PASRec, a diffusion-based framework that aligns ToI and IoI via a joint objective. Experiments on five benchmarks show superiority over eight state-of-the-art baselines under leave-one-out and temporal splits.
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