Generative Archetype-Grounded Item Representations for Sequential Recommendation
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 recom...
| Authors | Yifan Li, Jiahong Liu, Xinni Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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