Efficient and Effective Query Auto-Completion
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Abstract (arXiv)
Query Auto-Completion (QAC) is an ubiquitous feature of modern textual search systems, suggesting possible ways of completing the query being typed by the user. Efficiency is crucial to make the system have a real-time responsiveness when operating in the million-scale search space. Prior work has extensively advocated the use of a trie data structure for fast prefix-search operations in compact space. However, searching by prefix has little discovery power in that only completions that are prefixed by the query are returned. This may impact negatively the effectiveness of the QAC system, with a consequent monetary loss for real applications like Web Search Engines and eCommerce. In this work we describe the implementation that empowers a new QAC system at eBay, and discuss its efficiency/effectiveness in relation to other approaches at the state-of-the-art. The solution is based on the combination of an inverted index with succinct data structures, a much less explored direction in the literature. This system is replacing the previous implementation based on Apache SOLR that was not always able to meet the required service-level-agreement.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2025QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Enginescited by 1
- 2025Chat-Ghosting: A Comparative Study of Methods for Auto-Completion in Dialog Systemscited by 1
- 2022Small Character Models Match Large Word Models for Autocomplete Under Memory Constraintscited by 0
- 2026Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignmentcited by 0
- 2026Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment1 Feb 2026
- 2025Chat-Ghosting: A Comparative Study of Methods for Auto-Completion in Dialog Systems8 Jul 2025
- 2025QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines12 May 2025
- 2022Small Character Models Match Large Word Models for Autocomplete Under Memory Constraints6 Oct 2022