A Taxonomy of Query Auto Completion Modes

Krishnan, Unni; Moffat, Alistair; Zobel, Justin

doi:10.1145/3166072.3166081

Cited by 16 publications

(7 citation statements)

References 17 publications

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“…The QAC problem has been studied rather extensively since its popularization by Google around 2004. The interested reader can refer to the general surveys by Cai et al [4] and Krishnan et al [16] for an introduction to the problem.…”

Section: Related Workmentioning

confidence: 99%

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Efficient and Effective Query Auto-Completion

Gog,

Pibiri,

Venturini

2020

Preprint

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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-levelagreement.

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Section: Related Workmentioning

confidence: 99%

“…Following the taxonomy given by Krishnan et al [16], we have two major auto-completion query modesprefix-search and multiterm prefix-search -that have been implemented and are in widespread use. Our own implementation at eBay is no exception, thus these are the query modes we also focus on.…”

Section: Related Workmentioning

confidence: 99%

Efficient and Effective Query Auto-Completion

Gog,

Pibiri,

Venturini

2020

Preprint

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“…It is also possible to choose an initial candidate set based on popularity and then apply a second ranking criteria to obtain the final strings [15,16]. QAC implementation strategies vary based on how the partial query P is matched against the target strings [32]. A common approach is to use a trie [3,4,25,27] to retrieve candidates that have P as a prefix; or inverted index-based approaches [10,11,23] that offer completions independent of the ordering of the words in the partial query.…”

Section: Introductionmentioning

confidence: 99%

“…Error-tolerant QAC approaches [17,28,36,47] allow up to a fixed number of character mismatches to account for possible typing errors. User interactions are a key factor in implementation and evaluation of QAC systems [26,33,38] and have been captured using a wide range of models [31][32][33]37,38,43,44]. In particular, users are not limited to entering single characters, and can alter the partial query by selecting a completion or deleting characters already entered.…”

Section: Introductionmentioning

confidence: 99%

“…QAC system evaluations have typically been performed over large publicly available string collections [10,14,23], with strings taken sequentially from left-to-right to generate partial query sequences [10]. However this approach does not account for the full range of possible interactions [32,33]. In this work, we explore an approach to generation of synthetic partial query sequences that addresses this gap.…”

Section: Introductionmentioning

confidence: 99%

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Generation of Synthetic Query Auto Completion Logs

Krishnan

Moffat

Zobel

et al. 2020

Lecture Notes in Computer Science

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Privacy concerns can prohibit research access to large-scale commercial query logs. Here we focus on generation of a synthetic log from a publicly available dataset, suitable for evaluation of query auto completion (QAC) systems. The synthetic log contains plausible string sequences reflecting how users enter their queries in a QAC interface. Properties that would influence experimental outcomes are compared between a synthetic log and a real QAC log through a set of side-byside experiments, and confirm the applicability of the generated log for benchmarking the performance of QAC methods.

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