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Mobile information retrieval with search results clustering: Prototypes and evaluations

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  • Claudio Carpineto
  • Stefano Mizzaro
  • Giovanni Romano
  • Matteo Snidero

Abstract

Web searches from mobile devices such as PDAs and cell phones are becoming increasingly popular. However, the traditional list‐based search interface paradigm does not scale well to mobile devices due to their inherent limitations. In this article, we investigate the application of search results clustering, used with some success for desktop computer searches, to the mobile scenario. Building on CREDO (Conceptual Reorganization of Documents), a Web clustering engine based on concept lattices, we present its mobile versions Credino and SmartCREDO, for PDAs and cell phones, respectively. Next, we evaluate the retrieval performance of the three prototype systems. We measure the effectiveness of their clustered results compared to a ranked list of results on a subtopic retrieval task, by means of the device‐independent notion of subtopic reach time together with a reusable test collection built from Wikipedia ambiguous entries. Then, we make a cross‐comparison of methods (i.e., clustering and ranked list) and devices (i.e., desktop, PDA, and cell phone), using an interactive information‐finding task performed by external participants. The main finding is that clustering engines are a viable complementary approach to plain search engines both for desktop and mobile searches especially, but not only, for multitopic informational queries.

Suggested Citation

  • Claudio Carpineto & Stefano Mizzaro & Giovanni Romano & Matteo Snidero, 2009. "Mobile information retrieval with search results clustering: Prototypes and evaluations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(5), pages 877-895, May.
    • Handle: RePEc:bla:jamist:v:60:y:2009:i:5:p:877-895
    DOI: 10.1002/asi.21036
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    Cited by:

    1. Mariam Daoud & Jimmy Xiangji Huang, 2013. "Modeling geographic, temporal, and proximity contexts for improving geotemporal search," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 190-212, January.
    2. Sevgi Yigit‐Sert & Ismail Sengor Altingovde & Craig Macdonald & Iadh Ounis & Özgür Ulusoy, 2021. "Explicit diversification of search results across multiple dimensions for educational search," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(3), pages 315-330, March.

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