E-commerce search serves as a central interface connecting user demands with massive product inventories and plays a vital role in daily online shopping. However, it faces challenges, including highly ambiguous queries, noisy product texts with weak semantic order, and diverse user preferences, making it difficult to accurately capture user intent and fine-grained product semantics. Recent advances in large language models for semantic representation and contextual reasoning have created new opportunities to address these challenges. Nevertheless, existing e-commerce search datasets still suffer from notable limitations: queries are often heuristically constructed, cold-start users and long-tail products are filtered out, query and product texts are anonymized, and most datasets cover only a single stage of the search pipeline. These limitations hinder realistic and comprehensive evaluation and constrain research on LLM-based e-commerce search. To address them, we construct and release KuaiSearch, a large-scale e-commerce search dataset built upon real user interactions from the Kuaishou platform. KuaiSearch preserves authentic user queries and natural-language product texts, covers cold-start users and long-tail products, and provides dedicated benchmarks for three key tasks in the e-commerce search pipeline: recall, ranking, and relevance judgment. We conduct a comprehensive analysis of KuaiSearch from multiple perspectives, including products, users, and queries, and establish benchmarks across representative search tasks. Experimental results demonstrate that KuaiSearch provides a valuable foundation for real-world e-commerce search research. The dataset is publicly available at: https://github.com/benchen4395/KuaiSearch

翻译：暂无翻译