Query Suggestion by Query Search: A New Approach to User Support in Web Search

Shen, Jiang; Zilles, Sandra; Holte, Robert C.

doi:10.1109/wi-iat.2009.114

Cited by 7 publications

(12 citation statements)

References 14 publications

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“…Jiang et al [9] evaluated a set of query suggestions by the number of reference documents the set covers, either collectively ("MCC") or on average individually ("MEC"). 4 For example, if the set of query suggestions contains 10 queries that collectively cover a total of 75 reference documents, MCC for this set of suggestions would be 75.…”

Section: Objective and Evaluationmentioning

confidence: 99%

“…The aim of the system developed by Jiang et al [9], which we call Query Suggestion by Query Search (QSQS), is to find a set of 10 suggested queries that maximizes MCC and, as a secondary criterion, also maximizes MEC.…”

Section: Objective and Evaluationmentioning

confidence: 99%

“…We compare the MCC and MEC scores of the query suggestions produced by AC, BS, Q 0 AC and Q 0 BS on 50 short user queries (length 2 or less) drawn at random from the 250 short queries used in Jiang et al's experiments [9,7]. The average MCC and MEC scores over the 50 queries are shown in Table 1.…”

Section: Experiments Comparing Ac Bs Q 0 Ac and Q 0 Bsmentioning

confidence: 99%

“…The MCC and MEC differences between Q 0 AC and Q 0 BS are not statistically significant. As a final comparison we ran these two systems on 50 long user queries (length 3 or more) drawn at random from the 250 long queries used in Jiang et al's experiments [9,7]; the average MCC and MEC scores are shown in Table 2. The MCC and MEC differences between the two methods are statistically significant (p < 0.001).…”

Section: Experiments Comparing Ac Bs Q 0 Ac and Q 0 Bsmentioning

confidence: 99%

“…In this paper we follow the approach to query suggestion pioneered by Jiang et al [9,7] and present two query suggestion systems, called IQSQS and GQSQS, that are substantially superior to Jiang et al 's system according to their own evaluation measures (called MCC and MEC) and methodology. These systems are our paper's main contributions.…”

Section: Introductionmentioning

confidence: 99%

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Improved Query Suggestion by Query Search

Zhang¹,

Zilles

Holte

2012

Lecture Notes in Computer Science

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Abstract. At the Web Intelligence conference in 2009, Jiang, Zilles, and Holte introduced a novel approach to query suggestion based on query search (QSQS), as well as a system-centered evaluation method. For each potentially relevant document, QSQS creates a complex query-called a lexical alias for the documentthat ranks the document in its top 20. A technique called Query Search then builds query suggestions by simplifying the lexical alias. The present paper improves the state of the art by proposing two new query suggestion systems, IQSQS and GQSQS. Both replace the generation of lexical aliases by a simpler and more effective term selection process. They differ in their control structure: IQSQS builds query suggestions separately for each potentially relevant document, GQSQS builds them for a set of documents at once. Both our new systems substantially outperform QSQS in the measures introduced by Jiang et al. to evaluate QSQS; we achieve improvements of up to 30 percent in these measures for short user queries and up to 100 percent for long user queries. We show empirically that query expansion, which forces the user's query to be included in each suggested query, is significantly superior to allowing the system the freedom to include or exclude terms from the user's query at its discretion.

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Section: Objective and Evaluationmentioning

confidence: 99%

Section: Objective and Evaluationmentioning

confidence: 99%

Section: Experiments Comparing Ac Bs Q 0 Ac and Q 0 Bsmentioning

confidence: 99%

Section: Experiments Comparing Ac Bs Q 0 Ac and Q 0 Bsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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