skip to main content
10.1145/1871437.1871702acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
poster

An effective approach for mining mobile user habits

Authors Info & Claims
Published:26 October 2010Publication History

ABSTRACT

The user interaction with the mobile device plays an important role in user habit understanding, which is crucial for improving context-aware services. In this paper, we propose to mine the associations between user interactions and contexts captured by mobile devices, or behavior patterns for short, from context logs to characterize the habits of mobile users. Though several state-of-the-art studies have been reported for association mining, they cannot apply to behavior pattern mining due to the unbalanced occurrences of contexts and user interaction records. To this end, we propose a novel approach for behavior pattern mining which takes context logs as time ordered sequences of context records and takes into account the co-occurrences of contexts and interaction records in the whole time ranges of contexts. Moreover, we develop an Apriori-like algorithm for behavior pattern mining and improve the original algorithm in terms of efficiency by introducing the context hash tree. Last, we build a data collection system and collect the rich context data and interaction records of 50 recruited volunteers from their mobile devices. The extensive experiments on the collected real life data clearly validate the ability of our approach for mining effective behavior patterns.

References

  1. Agrawal, R. and Srikant, R. Fast algorithms for mining association rules. In VLDB'94, pages 487--499, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Cao, H., Bao, T., and Yang, Q. et al. An effective approach for mining mobile user habits. Technical report, 2009. http://dm.ustc.edu.cn/paperlist.htmlGoogle ScholarGoogle Scholar
  3. Han, J., Pei, J., and Yin, Y. Mining frequent patterns without candidate generation. In SIGMOD'00, pages 1--12. ACM, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Park, J. S., Chen, M., and Yu, P. S. An effective hash-based algorithm for mining association rules. In SIGMOD '95, pages 175--186, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. An effective approach for mining mobile user habits

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management
      October 2010
      2036 pages
      ISBN:9781450300995
      DOI:10.1145/1871437

      Copyright © 2010 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 26 October 2010

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • poster

      Acceptance Rates

      Overall Acceptance Rate1,861of8,427submissions,22%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader