Enhancing Time Awareness in Generative Recommendation

ACL ARR 2025 May Submission2991 Authors

Abstract: Generative recommendation has emerged as a promising paradigm that formulates the recommendations into a text-to-text generation task, harnessing the vast knowledge of large language models. However, existing studies focus on considering the sequential order of items and neglect to handle the temporal dynamics across items, which can imply evolving user preferences. To address this limitation, we propose a novel model, Generative Recommender Using Time awareness (GRUT), effectively capturing hidden user preferences via various temporal signals. We first introduce Time-aware Prompting, consisting of two key contexts. The user-level temporal context models personalized temporal patterns across timestamps and time intervals, while the item-level transition context provides transition patterns across users. We also devise Trend-aware Inference, a training-free method that enhances rankings by incorporating trend information about items with generation likelihood. Extensive experiments demonstrate that GRUT outperforms state-of-the-art models, with gains of up to 30.0% and 24.8% in Recall@5 and NDCG@5 across four benchmark datasets. The code will be available upon acceptance.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: Recommender Systems, Generative Recommendation, Temporal Recommendation, Large Language Models

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 2991