When and What to Recommend: Joint Modeling of Timing and Content for Active Sequential Recommendation

• URL: http://arxiv.org/abs/2511.18717v1
• Date: Mon, 24 Nov 2025 03:16:10 GMT
• Title: When and What to Recommend: Joint Modeling of Timing and Content for Active Sequential Recommendation
• Authors: Jin Chai, Xiaoxiao Ma, Jian Yang, Jia Wu,
• Abstract summary: We investigate active recommendation, which predicts the next interaction time and actively delivers items.<n>We propose PASRec, a diffusion-based framework that aligns ToI and IoI via a joint objective.
• Score: 15.851073753534521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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