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A Survey Of Context-Aware Mobile Recommendations

Author

Listed:
  • QI LIU

    (School of Computer Science and Technology, University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, P. R. China)

  • HAIPING MA

    (School of Computer Science and Technology, University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, P. R. China)

  • ENHONG CHEN

    (School of Computer Science and Technology, University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, P. R. China)

  • HUI XIONG

    (MSIS Department, Rutgers Business School, Rutgers University, Room 1076, 1 Washington Park, Newark, NJ, 07102, USA)

Abstract

Mobile recommender systems target on recommending the right product or information to the right mobile users at anytime and anywhere. It is well known that the contextual information is often the key for the performances of mobile recommendations. Therefore, in this paper, we provide a focused survey of the recent development of context-aware mobile recommendations. After briefly reviewing the state-of-the-art of recommender systems, we first discuss the general notion of mobile context and how the contextual information is collected. Then, we introduce the existing approaches to exploit contextual information for modeling mobile recommendations. Furthermore, we summarize several existing recommendation tasks in the mobile scenarios, such as the recommendations in the tourism domain. Finally, we discuss some key issues that are still critical in the field of context-aware mobile recommendations, including the privacy problem, the energy efficiency issues, and the design of user interfaces.

Suggested Citation

  • Qi Liu & Haiping Ma & Enhong Chen & Hui Xiong, 2013. "A Survey Of Context-Aware Mobile Recommendations," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 139-172.
    • Handle: RePEc:wsi:ijitdm:v:12:y:2013:i:01:n:s0219622013500077
    DOI: 10.1142/S0219622013500077
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    Cited by:

    1. Bahareh Rahmati & Mohammad Karim Sohrabi, 2019. "A Systematic Survey on High Utility Itemset Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1113-1185, July.

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