TSPORec: Token Selection via Preference Optimization for LLM-Based Sequential Recommendation
Large Language Models (LLMs) have emerged as powerful tools for improving recommendation systems. The effectiveness of LLMs arises from their ability to harness rich textual information and their capacity to model heterogeneous user preferences based on users' interaction history. However, due to the large-scale and deep architectures, LLM-based sequential recommendation approaches generally incur high inference costs, resulting in a low return on investment. To mitigate this cost, many existing approaches resort to using only the first few tokens of item descriptions, which inadvertently discards valuable information contained in the full text, thereby leading to suboptimal recommendatio...
| Authors | Wenqiao Zhu, Chao Xu, Haipang Wu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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