让搜索行为更懂推荐，通过分步推理筛选有用信号。
Bridging Search and Recommendation through Latent Cross Reasoning
- 先捕捉用户整体兴趣，再迭代推理搜索行为中的有效信号
- 在多个数据集上显著提升推荐效果，优于强基线模型
- 适合做搜索增强推荐的算法研究者和工业界应用
搜索与推荐（S&R）是现代在线平台的核心组件，但如何有效利用搜索行为提升推荐效果仍具挑战。用户搜索历史常包含噪声或无关信息，可能反而降低推荐性能；现有方法通常将搜索与推荐历史联合或分开编码，未显式识别真正有用的搜索行为。受人类决策过程启发——先确定推荐意图，再推理相关证据，我们提出一种潜在交叉推理框架：首先编码用户S&R历史以捕捉全局兴趣，然后迭代推理搜索行为，提取对推荐有益的信号。采用对比学习使潜在推理状态与目标项目对齐，并引入强化学习直接优化排序性能。在多个公开基准上的大量实验表明，该方法持续优于强基线，验证了推理在提升搜索感知推荐中的重要性。
原文摘要 · Abstract (English)
Search and recommendation (S&R) are fundamental components of modern online platforms, yet effectively leveraging search behaviors to improve recommendation remains a challenging problem. User search histories often contain noisy or irrelevant signals that can even degrade recommendation performance, while existing approaches typically encode S&R histories either jointly or separately without explicitly identifying which search behaviors are truly useful. Inspired by the human decision-making process, where one first identifies recommendation intent and then reasons about relevant evidence, we design a latent cross reasoning framework that first encodes user S&R histories to capture global interests and then iteratively reasons over search behaviors to extract signals beneficial for recommendation. Contrastive learning is employed to align latent reasoning states with target items, and reinforcement learning is further introduced to directly optimize ranking performance. Extensive experiments on public benchmarks demonstrate consistent improvements over strong baselines, validating the importance of reasoning in enhancing search-aware recommendation.
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