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A Context-Aware Model for Proactive Recommender Systems in the Tourism Domain

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Published:24 August 2015Publication History

ABSTRACT

A Proactive Recommender System (PRS) actively pushes recommendations to users when the current context seems appropriate. Despite the advantages of PRSs, especially in the mobile scenario where users could be provided with relevant items on-the-fly when needed, the area of PRSs is still unexplored with many challenges. In particular, it is crucial to identify the relevant items for the target users as well as to determine the right context for pushing these items, since otherwise the user acceptance, and therefore system success, will be negatively impacted. In this paper, we propose a new model that scores each item on two dimensions, preference fit and context fit, to proactively push relevant items to the target user in the right context. Furthermore, we present the preliminary design of a prototype of a mobile Point of Interest (POI) recommender which will be implemented in order to evaluate the practicality and effectiveness of our proposed model.

References

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      • Published in

        cover image ACM Conferences
        MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct
        August 2015
        697 pages
        ISBN:9781450336536
        DOI:10.1145/2786567

        Copyright © 2015 Owner/Author

        Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 August 2015

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        • poster
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        • Refereed limited

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        Overall Acceptance Rate202of906submissions,22%

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