# Item Cluster-aware Prompt Learning for Session-based Recommendation

arXiv: https://arxiv.org/abs/2410.04756 · HTML: https://mlsanity.com/paper/2410.04756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2106.05081/) (2021)
- [CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://mlsanity.com/paper/2204.11067/) (2022)
- [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2005.02844/) (2020)
- [Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2107.00852/) (2021)
- [Inter-Session Modeling for Session-Based Recommendation](https://mlsanity.com/paper/1706.07506/) (2017)

## Built on by (most cited first, 1 of 1)

- [Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback](https://mlsanity.com/paper/2605.29141/) (2026)

## Abstract (arXiv)

Session-based recommendation (SBR) aims to capture dynamic user preferences by analyzing item sequences within individual sessions. However, most existing approaches focus mainly on intra-session item relationships, neglecting the connections between items across different sessions (inter-session relationships), which limits their ability to fully capture complex item interactions. While some methods incorporate inter-session information, they often suffer from high computational costs, leading to longer training times and reduced efficiency. To address these challenges, we propose the CLIP-SBR (Cluster-aware Item Prompt learning for Session-Based Recommendation) framework. CLIP-SBR is composed of two modules: 1) an item relationship mining module that builds a global graph to effectively model both intra- and inter-session relationships, and 2) an item cluster-aware prompt learning module that uses soft prompts to integrate these relationships into SBR models efficiently. We evaluate CLIP-SBR across eight SBR models and three benchmark datasets, consistently demonstrating improved recommendation performance and establishing CLIP-SBR as a robust solution for session-based recommendation tasks.