Bridging Search and Recommendation through Latent Cross Reasoning

TL;DR

This paper introduces a latent cross reasoning framework that improves search-aware recommendation by explicitly identifying useful search behaviors through iterative reasoning, contrastive learning, and reinforcement learning, leading to better recommendation performance.

Contribution

It proposes a novel reasoning-based approach that explicitly models useful search behaviors for recommendation, addressing noise and relevance issues in user search histories.

Findings

Consistent improvements over strong baselines on public benchmarks

Effective use of contrastive learning to align reasoning states with target items

Reinforcement learning enhances ranking performance

Abstract

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.