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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].

References

  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)

    Article  Google Scholar 

  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)

  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)

  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)

  5. Ariyasu, K., Fujisawa, H., Sunasaki, S.: Twitter analysis algorithms for intelligence circulation system. Multimedia Syst. 19(6), 477–491 (2013)

    Article  Google Scholar 

  6. Baltrunas, L.: Context-Aware Collaborative Filtering Recommender Systems. Ph.D. thesis, Free University of Bozen-Bolzano (2011)

  7. Baltrunas, L., Ricci, F.: Ontext-dependent items generation in collaborative filtering. In: Proceedings of the Workshop on Context-Aware Recommender Systems (2009)

  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)

  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)

  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)

  11. Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mobile Comput. 5(4), 277–298 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  14. Dey, A.K.: Context-aware computing: the CyberDesk project. In: Proceedings of the Spring Symposium on Intelligent Environments (1998)

  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)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

  20. Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89–115 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  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)

    MathSciNet  MATH  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  29. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrival. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  30. Mesnage, C.S., Rafiq, A.: Music discovery with social networks. Proc. Workshop Music Recomm. Discovery 793, 1–6 (2011)

    Google Scholar 

  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)

  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)

  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)

  34. Wang, X., Rosenblum, D., Wang, Y.: Context-Aware Mobile Music Recommendation for Daily Activities, Proceedings of ACM Multimedia (2012)

  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)

  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)

    Article  Google Scholar 

  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)

  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)

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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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