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RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment

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Abstract

With an ever-increasing accessibility to different multimedia contents in real-time, it is difficult for users to identify the proper resources from such a vast number of choices. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is a need to reinforce the recommendation process in a systematic way, with context-adaptive information. The contributions of this paper are twofold. First, we propose a framework, called RecAm, which enables the collection of contextual information and the delivery of resulted recommendation by adapting the user’s environment using Ambient Intelligent (AmI) Interfaces. Second, we propose a recommendation model that establishes a bridge between the multimedia resources, user joint preferences, and the detected contextual information. Hence, we obtain a comprehensive view of the user’s context, as well as provide a personalized environment to deliver the feedback. We demonstrate the feasibility of RecAm with two prototypes applications that use contextual information for recommendations. The offline experiment conducted shows the improvement of delivering personalized recommendations based on the user’s context on two real-world datasets.

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Notes

  1. http://www.last.fm/.

  2. The MovieLens dataset can be downloaded from: http://www.grouplens.org/node/73 [last visited December 2013].

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Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the research group Project no. RGP-VPP-049. The authors also extend their appreciation to Ayman Barnawi for his valuable efforts in the implementation of the proposed RecMirror application.

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Authors and Affiliations

  1. College of Computer and Information Sciences (CCIS), King Saud University, Riyadh, Saudi Arabia

    Mohammed F. Alhamid, M. Anwar Hossain & Abdulhameed Alelaiwi

  2. Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

    Majdi Rawashdeh

  3. Multimedia Computing Research Laboratory (MCRlab), School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward, Ottawa, ON, K1N 6N5, Canada

    Haiwei Dong & Abdulmotaleb El Saddik

Authors
  1. Mohammed F. Alhamid
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  2. Majdi Rawashdeh
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  3. Haiwei Dong
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  4. M. Anwar Hossain
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  5. Abdulhameed Alelaiwi
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  6. Abdulmotaleb El Saddik
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Corresponding author

Correspondence to Mohammed F. Alhamid.

Additional information

Communicated by B. Prabhakaran.

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Alhamid, M.F., Rawashdeh, M., Dong, H. et al. RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment. Multimedia Systems 22, 587–601 (2016). https://doi.org/10.1007/s00530-015-0469-2

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  • DOI: https://doi.org/10.1007/s00530-015-0469-2

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