Multimedia Systems

, Volume 22, Issue 5, pp 587–601 | Cite as

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

  • Mohammed F. Alhamid
  • Majdi Rawashdeh
  • Haiwei Dong
  • M. Anwar Hossain
  • Abdulhameed Alelaiwi
  • Abdulmotaleb El Saddik
Regular Paper
First Online:
Received:
Accepted:

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.

Keywords

Context media search Context-aware recommendation  Collaborative context Context awareness Ambient intelligent 

Notes

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.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Agarwal, D., Chen, B.C., Long, B.: Localized factor models for multi-context recommendation. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining—KDD ’11, c:609 (2011)Google Scholar
  3. 3.
    Alhamid, M.F., Rawashdeh, M., Al Osman, H., El Saddik, A.: Leveraging biosignal and collaborative filtering for context-aware recommendation. In: Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare. ACM (2013)Google Scholar
  4. 4.
    Alhamid, M.F., Rawashdeh, M., El Saddik, A.: Towards context-aware recommendations of multimedia in an ambient intelligence environment. In: Proceedings of the 9th Workshop on Multimedia Information Processing and Retrieval (2013)Google Scholar
  5. 5.
    Ariyasu, K., Fujisawa, H., Sunasaki, S.: Twitter analysis algorithms for intelligence circulation system. Multimedia Syst. 19(6), 477–491 (2013)CrossRefGoogle Scholar
  6. 6.
    Baltrunas, L.: Context-Aware Collaborative Filtering Recommender Systems. Ph.D. thesis, Free University of Bozen-Bolzano (2011)Google Scholar
  7. 7.
    Baltrunas, L., Ricci, F.: Ontext-dependent items generation in collaborative filtering. In: Proceedings of the Workshop on Context-Aware Recommender Systems (2009)Google Scholar
  8. 8.
    Cai, R., Zhang, C., Wang, C., Zhang, L., Ma, W.: MusicSense : Contextual Music Recommendation using Emotional Allocation Modeling. In: Proceedings of the 15th International Conference on Multimedia, pp. 553–556 (2007)Google Scholar
  9. 9.
    Ceccaroni, L., Verdaguer, X.: Magical Mirror: multimedia, interactive services in home automation. In: Proceedings of Workshop on Environments for Personalized Information Access, pp. 10–21 (2004)Google Scholar
  10. 10.
    Chen, C.C., Huang, T.C., Park, J.J., Yen, N.Y.: Real-time smartphone sensing and recommendations towards context-awareness shopping. Multimedia Syst., 1–12 (2013)Google Scholar
  11. 11.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mobile Comput. 5(4), 277–298 (2009)CrossRefGoogle Scholar
  12. 12.
    de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Rueda-Morales, M.: Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks. Int. J. Approx. Reason. 51(7), 785–799 (2010)CrossRefGoogle Scholar
  13. 13.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. Ser. B 39(1), 1–38 (1977)MathSciNetMATHGoogle Scholar
  14. 14.
    Dey, A.K.: Context-aware computing: the CyberDesk project. In: Proceedings of the Spring Symposium on Intelligent Environments (1998)Google Scholar
  15. 15.
    Dey, Anind, Abowd, Gregory, Salber, Daniel: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human Comput. Interact. 16(2), 97–166 (2001)CrossRefGoogle Scholar
  16. 16.
    Ducatel, K., Bogdanowicz, M., Scapolo, F.: Scenarios for ambient intelligence in 2010. tech. report, Information Society Technologies Advisory Group, Institute of Prospective Technological Studies (2001)Google Scholar
  17. 17.
    Elliott, G.T., Tomlinson, B.: PersonalSoundtrack : context-aware playlists that adapt to user pace. CHI’06 Extended Abstracts on Human Factors in Computing Systems, pp. 736–741 (2006)Google Scholar
  18. 18.
    Gori, M., Pucci, A., Roma, V., Siena, I.: Itemrank: a random-walk based scoring algorithm for recommender engines. In the 20th international joint conference on Artificial Intelligences, pp. 2766–2771 (2007)Google Scholar
  19. 19.
    Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pages 50–57 (1999)Google Scholar
  20. 20.
    Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89–115 (2004)CrossRefGoogle Scholar
  21. 21.
    Hossain, M.A., Atrey, P.K., El Saddik, A.: Gain-based selection of ambient media services in pervasive environments. Mobile Netw. Appl. 13(6), 599–613 (2008)CrossRefGoogle Scholar
  22. 22.
    Hossain, M.A., Parra, J., Atrey, P.K., El Saddik, A.: A framework for human-centered provisioning of ambient media services. Multimedia Tools Appl. 44(3), 407–431 (2009)CrossRefGoogle Scholar
  23. 23.
    Hu, Y., Ogihara, M.: Nextone player: a music recommendation system based on user behavior. In: Proceedings of 12th International Society for Music Information Retirval Conference, pp. 103–108 (2011)Google Scholar
  24. 24.
    Hyung, Z., Lee, M.A., Lee, K.: Music Recommendation Based on Text Mining. In: Proceedings of the 2nd International Conference on Advances in Information Mining and Management, pp. 129–134 (2012)Google Scholar
  25. 25.
    Jäschke, R., Marinho, L., Hotho, A.: Lars Schmidt-Thieme, and Gerd Stumme. Tag recommendations in social bookmarking systems. AI Commun. 21(4), 231–247 (2008)MathSciNetMATHGoogle Scholar
  26. 26.
    Karatzoglou, A., Amatriain, X.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the fourth ACM conference on Recommender systems (2010)Google Scholar
  27. 27.
    Kim, H.N., Bloess, M., El Saddik, A.: Folkommender: a group recommender system based on a graph-based ranking algorithm. Multimedia Syst. 19(6), 509–525 (2012)CrossRefGoogle Scholar
  28. 28.
    Kim, J., Lee, D., Chung, K.Y.: Item recommendation based on context-aware model for personalized u-healthcare service. Multimedia Tools Appl. 71(2), 855–872 (2014)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrival. Cambridge University Press, Cambridge (2008)CrossRefMATHGoogle Scholar
  30. 30.
    Mesnage, C.S., Rafiq, A.: Music discovery with social networks. Proc. Workshop Music Recomm. Discovery 793, 1–6 (2011)Google Scholar
  31. 31.
    Miraoui, M., Tadj, C., Fattahi, J., Amar,C. B.: Dynamic context-aware and limited resources-aware service adaptation for pervasive computing. Advances in Software Engineering, p. 7 (2011)Google Scholar
  32. 32.
    Shi, Y., Karatzoglou, A., Baltrunas, L.: TFMAP: Optimizing MAP for top-n context-aware recommendation. In: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. ACM (2012)Google Scholar
  33. 33.
    Shi, Y., Larson, M., Hanjalic, A.: Mining mood-specific movie similarity with matrix factorization for context-aware recommendation. In: Proceedings of the Workshop on Context-Aware Movie Recommendation, pp. 34–40 (2010)Google Scholar
  34. 34.
    Wang, X., Rosenblum, D., Wang, Y.: Context-Aware Mobile Music Recommendation for Daily Activities, Proceedings of ACM Multimedia (2012)Google Scholar
  35. 35.
    Woerndl, W., Schueller, C., Wojtech, R., Gmbh, U.: A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications. In: Data Engineering Workshop, 2007 IEEE 23rd International Conference on, pp. 871–878 (2007)Google Scholar
  36. 36.
    Zhiwen, Y., Zhou, X., Zhang, D., Chin, C.Y., Wang, X., Men, J.: Supporting context-aware media recommendations for smart phones. Pervasive Comput. 5(3), 68–75 (2006)CrossRefGoogle Scholar
  37. 37.
    Zanardi, V., Capra, L.: Social ranking: uncovering relevant content using tag-based recommender systems. in: Proceedings of the 2008 ACM conference on Recommender systems, pp. 51–58 (2008)Google Scholar
  38. 38.
    Zangerle, E., Gassler, W., Specht, G.: Exploiting Twitter’s Collective Knowledge for Music Recommendations. In: Proc. WWW Workshop: #MSM2012 (2nd Workshop on Making Sense of Microposts), pp. 14–17 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Mohammed F. Alhamid
    • 1
  • Majdi Rawashdeh
    • 2
  • Haiwei Dong
    • 3
  • M. Anwar Hossain
    • 1
  • Abdulhameed Alelaiwi
    • 1
  • Abdulmotaleb El Saddik
    • 3
  1. 1.College of Computer and Information Sciences (CCIS)King Saud UniversityRiyadhSaudi Arabia
  2. 2.Division of EngineeringNew York University Abu DhabiAbu DhabiUnited Arab Emirates
  3. 3.Multimedia Computing Research Laboratory (MCRlab), School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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