ReList: A Multi-objective Reasoning Framework for Diversified Listwise Query Recommendation

Shuxian Bi, Chenxu Wang, Wenjie Wang, Yueqi Mou, Fuli Feng, Tang Biao, Peng Yan

Keywords: Query Generation, Large Language Model, Reasoning, Related Search

TL;DR: ReList transforms query suggestion from redundant pointwise retrieval to reasoning-enhanced listwise generation. By integrating reasoning activation with multi-objective reinforcement learning, it achieves SOTA diversity and engagement

Abstract: Related search query recommendation is essential for enhancing user engagement and information discovery on digital platforms. While Large Language Models (LLMs) have shifted the field toward generative retrieval, existing methods suffer from two primary limitations: (1) pointwise generation via beam search often leads to semantic redundancy and wasted retrieval quota, and (2) current listwise approaches lack explicit reasoning, relying on superficial click-through rate (CTR) rewards. In this paper, we propose ReList, a novel framework that transforms related search into a reasoning-enhanced listwise generation task. ReList follows a two-stage training paradigm: first, Reasoning Activation constructs a high-quality dataset by back-translating diverse query lists into Chain-of-Thought (CoT) rationales; second, Alternative Training iteratively evolves the model using Reinforcement Learning with a Gated Multi-Objective Reward and a Corrective SFT mechanism to handle hard samples. Experimental results on real-world search benchmarks and online A/B tests demonstrate that ReList significantly outperforms state-of-the-art methods in both query diversity and user engagement, providing more insightful and logically grounded query recommendations.

Submission Type: Deployed

Submission Number: 329