Query exhaustivity, relevance feedback and search success in automatic and interactive query expansion

被引:9
|
作者
Vakkari, P [1 ]
Jones, S
MacFarlane, A
Sormunen, E
机构
[1] Univ Tampere, Dept Informat Studies, FIN-33101 Tampere, Finland
[2] City Univ London, Dept Informat Sci, London EC1V 0HB, England
关键词
searching; query languages;
D O I
10.1108/00220410410522016
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study explored how the expression of search facets and relevance feedback (RF) by users was related to search success in interactive and automatic query expansion in the course of the search process. Search success was measured both in the number of relevant documents retrieved, whether identified by users or not. Research design consisted of 26 users searching for four TREC topics in Okapi IR system, half of the searchers using interactive and half automatic query expansion based on RE The search logs were recorded, and the users filled in questionnaires for each. topic concerning various features of searching. The results showed that the exhaustivity of the query was the most significant predictor of search success. Interactive expansion led to better search success than automatic expansion if all retrieved relevant items were counted, but there was no difference between the methods if only those items recognised relevant by users were observed. The analysis showed that the difference was facilitated by the liberal relevance criterion used in TREC not favouring highly relevant documents in evaluation.
引用
收藏
页码:109 / 127
页数:19
相关论文
共 50 条
  • [41] Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval
    Chang, CH
    Hsu, CC
    COMPUTER NETWORKS AND ISDN SYSTEMS, 1998, 30 (1-7): : 621 - 623
  • [42] Pseudo-relevance feedback and statistical query expansion for web snippet generation
    Ko, Youngjoong
    An, Hongkuk
    Seo, Jungyun
    INFORMATION PROCESSING LETTERS, 2008, 109 (01) : 18 - 22
  • [43] Semantics-aware query expansion using pseudo-relevance feedback
    Singh, Pankaj
    Bhowmick, Plaban Kumar
    JOURNAL OF INFORMATION SCIENCE, 2023,
  • [44] Pseudo-relevance feedback based query expansion using boosting algorithm
    Rasheed, Imran
    Banka, Haider
    Khan, Hamaid Mahmood
    ARTIFICIAL INTELLIGENCE REVIEW, 2021, 54 (08) : 6101 - 6124
  • [45] A New Approach for Automatic Query Expansion
    Hmeidi, Ismail
    Al-Badarneh, Amer
    Al-Qtaish, Ahmad A.
    BUSINESS TRANSFORMATION THROUGH INNOVATION AND KNOWLEDGE MANAGEMENT: AN ACADEMIC PERSPECTIVE, VOLS 3 AND 4, 2010, : 1975 - 1989
  • [46] Neural Query Expansion for Code Search
    Liu, Jason
    Kim, Seohyun
    Murali, Vijayaraghavan
    Chaudhuri, Swarat
    Chandra, Satish
    PROCEEDINGS OF THE 3RD ACM SIGPLAN INTERNATIONAL WORKSHOP ON MACHINE LEARNING AND PROGRAMMING LANGUAGES (MAPL '19), 2019, : 29 - 37
  • [47] Effective Query Expansion for Federated Search
    Shokouhi, Milad
    Azzopardi, Leif
    Thomas, Paul
    PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 2009, : 427 - 434
  • [48] Efficient Query Expansion for Advertisement Search
    Wang, Haofen
    Liang, Yan
    Fu, Linyun
    Xue, Gui-Rong
    Yu, Yong
    PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 2009, : 51 - 58
  • [49] Synonymy for query expansion in information search
    Chishman, Rove
    Vieira, Renata
    Alves, Isa Mara
    Rigo, Sandro
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2003, 2902 : 445 - 449
  • [50] Synonymy for query expansion in information search
    Chishman, R
    Vieira, R
    Alves, IM
    Rigo, S
    PROGRESS IN ARTIFICIAL INTELLIGENCE-B, 2003, 2902 : 445 - 449