[Paper] Generative Archetype-Grounded Item Representations for Sequential Recommendation
Source: arXiv - 2606.11023v1
Overview
Sequential recommendation aims to predict users’ next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck. While pre-trained large language models (LLMs) can provide rich semantic representations, existing approaches only rely on static encoding of fixed attributes, overlooking the crucial role of target audiences in defining item identity. Moreover, the semantic space struggles to reflect actual user behavior, resulting in a significant gap between semantic representations and behavioral patterns. To address these limitations, we propose GenAIR, a general framework that empowers sequential recommendation with Generative Archetype-grounded Item Representations. Specifically, we first leverage an LLM to analyze item metadata and infer textual description of the Archetype, which represents the conceptual profile of the item’s ideal target audience. We then extract the corresponding embeddings in a single forward pass. Further, to ground these generative archetypes in real-world behavior, we introduce a behavioral calibration objective, which explicitly incorporates behavioral signals from actual interactions. This objective adjusts the structure of the embedding space to reflect empirical patterns. GenAIR enables seamless integration with most existing models while maintaining high efficiency. Comprehensive experiments conducted on three real-world datasets demonstrate that GenAIR significantly improves the performance of various sequential recommendation models and consistently outperforms state-of-the-art baseline approaches. Implementation codes are available at https://github.com/AI-Santiago/GenAIR.
Key Contributions
This paper presents research in the following areas:
- cs.IR
- cs.CL
- cs.LG
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.IR.
Authors
- Yifan Li
- Jiahong Liu
- Xinni Zhang
- Hao Chen
- Yankai Chen
- Wenhao Yu
- Jianting Chen
- Irwin King
Paper Information
- arXiv ID: 2606.11023v1
- Categories: cs.IR, cs.CL, cs.LG
- Published: June 9, 2026
- PDF: Download PDF