Bridging Search and Recommendation through Latent Cross Reasoning

2025-08-06

AI Summary

This paper addresses the problem of noisy search behaviors degrading recommendation performance in search-augmented recommendation systems. To tackle this, we propose the Implicit Cross-Reasoning (ICR) framework, which first models users’ global interests and then iteratively performs implicit reasoning to dynamically identify search signals beneficial for recommendation. ICR innovatively integrates contrastive learning—aligning the reasoning state representations with target item embeddings—and reinforcement learning—optimizing ranking metrics end-to-end—to emulate the human cognitive process of “retrieval → reflection → decision.” Extensive experiments on multiple public benchmarks demonstrate that ICR significantly outperforms state-of-the-art baselines in both recommendation accuracy and robustness, validating its effectiveness in mitigating search noise while enhancing recommendation quality.

Technology Category

• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Learning Preferences or Rankings

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Semantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization

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.

Problem

• Leveraging search behaviors to improve recommendation performance
• Identifying useful search signals from noisy user histories
• Aligning latent reasoning with target items for better ranking

Innovation

• Latent cross reasoning for S&R alignment
• Contrastive learning for target alignment
• Reinforcement learning for ranking optimization