Interactive Search Result Clustering: A Study of User Behavior and Retrieval Effectiveness

被引:0
|
作者
Gong, Xuemei [1 ]
Ke, Weimao [1 ]
Zhang, Yan [2 ]
Broussard, Ramona [2 ]
机构
[1] Drexel Univ, Coll Informat Sci & Technol, iSchool Drexel, Lab Info Network & Comp Studies, 3141 Chest St, Philadelphia, PA 19104 USA
[2] Univ Texas Austin, Sch Informat, Austin, TX 78701 USA
关键词
Text clustering; Search result presentation; Scatter/Gather; User interface; Exploratory search; Interaction; Relevance;
D O I
暂无
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Scatter/Gather is a document browsing and information retrieval method based on document clustering. It is designed to facilitate user articulation of information needs through iterative clustering and interactive browsing. This paper reports on a study that investigated the effectiveness of Scatter/Gather browsing for information retrieval. We conducted a within-subject user study of 24 college students to investigate the utility of a Scatter/Gather system, to examine its strengths and weaknesses, and to receive feedback from users on the system. Results show that the clustering-based Scatter/Gather method was more difficult to use than the classic information retrieval systems in terms of user perception. However, clustering helped the subjects accomplish the tasks more efficiently. Scatter/Gather clustering was particularly useful in helping users finish tasks that they were less familiar with and allowed them to search with fewer words. Scatter/Gather tended to be more useful when it was more difficult for the user to do query specification for an information need. Topic familiarity and specificity had significant influences on user perceived retrieval effectiveness. The influences appeared to be greater with the Scatter/Gather system compared to a classic search system. Topic familiarity also had significant influences on query formulation.
引用
收藏
页码:167 / 170
页数:4
相关论文
共 50 条
  • [21] Web information retrieval with result set clustering
    Silva, MJ
    Martins, B
    PROGRESS IN ARTIFICIAL INTELLIGENCE-B, 2003, 2902 : 450 - 454
  • [22] Interactive search in image retrieval: a survey
    Thomee, Bart
    Lew, Michael S.
    INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL, 2012, 1 (02) : 71 - 86
  • [23] User centred interactive search in the humanities
    Warwick, C
    Rimmer, J
    Blandford, A
    Buchanan, G
    PROCEEDINGS OF THE 5TH ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, PROCEEDINGS, 2005, : 400 - 400
  • [24] USER INTENTION MODELING FOR INTERACTIVE IMAGE RETRIEVAL
    Cui, Jingyu
    Wen, Fang
    Tang, Xiaoou
    2010 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME 2010), 2010, : 1517 - 1522
  • [25] Using clustering and classification approaches in interactive retrieval
    Wu, MF
    Fuller, M
    Wilkinson, R
    INFORMATION PROCESSING & MANAGEMENT, 2001, 37 (03) : 459 - 484
  • [26] USER ADAPTATION IN INTERACTIVE INFORMATION-RETRIEVAL
    MEADOW, CT
    JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE, 1983, 34 (04): : 289 - 291
  • [27] Post-retrieval search hit clustering to improve information retrieval effectiveness: Two digital forensics case studies
    Beebe, Nicole Lang
    Clark, Jan Guynes
    Dietrich, Glenn B.
    Ko, Myung S.
    Ko, Daijin
    DECISION SUPPORT SYSTEMS, 2011, 51 (04) : 732 - 744
  • [28] A New Approach to Search Result Clustering and Labeling
    Turel, Anil
    Can, Fazli
    INFORMATION RETRIEVAL TECHNOLOGY, 2011, 7097 : 283 - 292
  • [29] Identifying Evolutionary Approach for Search Result Clustering
    Mehrotra, Shashi
    Kohli, Shruti
    PROCEEDINGS OF THE 10TH INDIACOM - 2016 3RD INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT, 2016, : 3778 - 3782
  • [30] Application of Clustering for Improving Search Result of a Website
    Mehrotra, Shashi
    Kohli, Shruti
    INFORMATION SYSTEMS DESIGN AND INTELLIGENT APPLICATIONS, VOL 2, INDIA 2016, 2016, 434 : 349 - 356