2026/02/27 by Gui Ling, Weiyuan Li, Yue Jiang +7 · 1 voice
Computer Science · #Advanced Database Systems and Queries #Data Management and Algorithms #Data retrieval #Document retrieval #Economic shortage #Foundation (evidence) #Information Retrieval and Search Behavior #Mainstream #Product (mathematics) #Query language #Ranking (information retrieval) #Set (abstract data type) #cs.IR
paper · pdf · doi:10.1145/3805712.3808409
arxiv published 2026/02/27 · arxiv updated 2026/04/27 · openalex publication_date 2026/07/10 · openalex created_date 2026/07/11 · openalex updated_date 2026/07/29
Product retrieval is the backbone of e-commerce search: for each user query, it identifies a high-recall candidate set from billions of items, laying the foundation for high-quality ranking and user experience. Despite extensive optimization for mainstream queries, existing systems still struggle with long-tail queries, especially knowledge-intensive ones. These queries exhibit diverse linguistic patterns, often lack explicit purchase intent, and require domain-specific knowledge reasoning for accurate interpretation. They also suffer from a shortage of reliable behavioral logs, which makes such queries a persistent challenge for retrieval optimization.