TSPORec: Token Selection via Preference Optimization for Sequential Recommendation with Large Language Models

ACL ARR 2026 January Submission3643 Authors

Keywords: Token Selection, Preference Optimization, LLM-Based Recommendation

Abstract:

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 recommendation performance. To address this limitation, we propose a novel Token Selection approach for Preference Optimization in LLM-based sequential Recommendation, i.e.,
TSPORec, which accurately pinpoints informative tokens throughout the entire textual content to improve recommendation performance. Specifically, we design a three-stage pipeline to select informative tokens and introduce a novel proxy reward to facilitate the implementation. TSPORec not only enhances recommendation
performance but also improves computational efficiency. Extensive experiments across two models and datasets demonstrate the superb performance (up to 31.25%) and efficiency (up to 63.4%) of our approach compared with six
baseline approaches.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: applications, LLM Efficiency, reinforcement learning, human-in-the-loop

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency

Languages Studied: english

Submission Number: 3643