SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911593328017408 |
|---|---|
| author | Lee, Gyuseok Kweon, Wonbin Yue, Zhenrui Liu, Yaokun Liu, Yifan Yoon, Susik Wang, Dong Kang, SeongKu |
| author_facet | Lee, Gyuseok Kweon, Wonbin Yue, Zhenrui Liu, Yaokun Liu, Yifan Yoon, Susik Wang, Dong Kang, SeongKu |
| contents | Large language models (LLMs) have enhanced conventional recommendation models via user profiling, which generates representative textual profiles from users' historical interactions. However, their direct application to session-based recommendation (SBR) remains challenging due to severe session context scarcity and poor scalability. In this paper, we propose SPRINT, a scalable SBR framework that incorporates reliable and informative intents while ensuring high efficiency in both training and inference. SPRINT constrains LLM-based profiling with a global intent pool and validates inferred intents based on recommendation performance to mitigate noise and hallucinations under limited context. To ensure scalability, LLMs are selectively invoked only for uncertain sessions during training, while a lightweight intent predictor generalizes intent prediction to all sessions without LLM dependency at inference time. Experiments on real-world datasets show that SPRINT consistently outperforms state-of-the-art methods while providing more explainable recommendations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00570 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation Lee, Gyuseok Kweon, Wonbin Yue, Zhenrui Liu, Yaokun Liu, Yifan Yoon, Susik Wang, Dong Kang, SeongKu Information Retrieval Large language models (LLMs) have enhanced conventional recommendation models via user profiling, which generates representative textual profiles from users' historical interactions. However, their direct application to session-based recommendation (SBR) remains challenging due to severe session context scarcity and poor scalability. In this paper, we propose SPRINT, a scalable SBR framework that incorporates reliable and informative intents while ensuring high efficiency in both training and inference. SPRINT constrains LLM-based profiling with a global intent pool and validates inferred intents based on recommendation performance to mitigate noise and hallucinations under limited context. To ensure scalability, LLMs are selectively invoked only for uncertain sessions during training, while a lightweight intent predictor generalizes intent prediction to all sessions without LLM dependency at inference time. Experiments on real-world datasets show that SPRINT consistently outperforms state-of-the-art methods while providing more explainable recommendations. |
| title | SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.00570 |