Journal of Intelligent Information Systems

, Volume 46, Issue 3, pp 499–516 | Cite as

Towards context-aware media recommendation based on social tagging

  • Mohammed F. AlhamidEmail author
  • Majdi Rawashdeh
  • M. Anwar Hossain
  • Abdulhameed Alelaiwi
  • Abdulmotaleb El Saddik


Nowadays, we consume different types of multimedia contents using a substantial variety of devices and platforms. These devices run independently with limitations in sharing content and considerable efforts are spent to satisfy users. On one hand, individual persons have different reasons for browsing or consuming multimedia content including entertainment, maintaining individual’s well-being, or learning. On the other hand, smart engines, such as those built to provide recommendations, browse and manipulate the multimedia collections for a variety of business purposes such as advertising, studying consumer behavior and other business related objectives. It remains challenging to select the most appropriate, favorite, or appropriately related content with the help of contextual information from the traditional user-item datasets. In view of that, it is worthwhile examining context in a way that can be highly beneficial to personalized recommendation.

Detecting the user...


Personalized search Context media search Context-aware recommendation Collaborative context Context awareness 



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.


  1. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.CrossRefGoogle Scholar
  2. Adomavicius, G., & Tuzhilin, A. (2011). Context-aware recommender systems. In Recommender systems handbook (pp. 217–253). Springer.Google Scholar
  3. Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems, 23(1), 103–145.CrossRefGoogle Scholar
  4. Agarwal, D., Chen, B.-C., & Long, B. (2011). Localized factor models for multi-context recommendation. In Proceedings of the 17th ACM international conference on knowledge discovery and data mining (p. 609).Google Scholar
  5. Alhamid, M. F., Rawashdeh, M., Al Osman, H., & El Saddik, A. (2013). 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 (pp. 41–48).Google Scholar
  6. Amato, F., Chianese, A., & Moscato, V. (2012). SNOPS: a smart environment for cultural heritage applications. In Proceedings of the 12th international workshop on Web information and data management (pp. 49–56).Google Scholar
  7. Baltrunas, L., & Ricci, F. (2009). Context-dependent items generation in collaborative filtering. In Proceedings of the workshop on context-aware recommender systems.Google Scholar
  8. Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., Lüke, K.-H., & Schwaiger, R. (2011). Incarmusic: Context-aware music recommendations in a car. In E-Commerce and web technologies, lecture notes in business information processing (pp. 89–100). Springer.Google Scholar
  9. Chen, A. (2005). Context-aware collaborative filtering system: Predicting the user’s preference in the ubiquitous computing environment. Location-and Context-Awareness, 1110–1111.Google Scholar
  10. Deshpande, M., & Karypis, G. (2004). Item-based top- N recommendation algorithms. ACM Transactions on Information Systems, 22(1), 143–177.CrossRefGoogle Scholar
  11. Haveliwala, T., Kamvar, S., & Jeh, G. (2003). An analytical comparison of approaches to personalizing PageRank.Google Scholar
  12. Hossain, M.A., Atrey, P.K., & El Saddik, A. (2008). Gain-based selection of ambient media services in pervasive environments. Mobile Networks and Applications, 13(6), 599–613.CrossRefGoogle Scholar
  13. Hu, Y. (2012). A music recommendation system based on user behaviors and genre classification, Ph.D. dissertation, University of Miami.Google Scholar
  14. Hu, Y., & Ogihara, M. (2011). Nextone player: A music recommendation system based on user behavior. In Proceedings of the 12th international society for music information retirval conference (pp. 103–108).Google Scholar
  15. Hyung, Z., Lee, M., & Lee, K. (2012). Music recommendation based on text mining. In Proceedings of the 2nd international conference on advances in information mining and management (pp. 129–134).Google Scholar
  16. Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., & Stumme, G. (2008). Tag recommendations in social bookmarking systems. AI Communications, 21(4), 231–247.MathSciNetzbMATHGoogle Scholar
  17. Karatzoglou, A., & Amatriain, X. (2010). Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the 4th ACM conference on recommender systems.Google Scholar
  18. Kim, H., & Choi, Y. (2011). EmoSens: Affective entity scoring, a novel service recommendation framework for mobile platform. In Proceedings of the 5th ACM conference on recommender systems.Google Scholar
  19. Kim, B.M., Li, Q., Park, C.S., Kim, S.G., & Kim, J.Y. (2006). A new approach for combining content-based and collaborative filters. Journal of Intelligent Information Systems, 27(1), 79–91.CrossRefGoogle Scholar
  20. Kim, H.-N., Rawashdeh, M., Alghamdi, A., & El Saddik, A. (2012). Folksonomy-based personalized search and ranking in social media services. Information Systems, 37(1), 61–76.CrossRefGoogle Scholar
  21. Lee, S., Song, S.-I., Kahng, M., Lee, D., & Lee, S.-G. (2011). Random walk based entity ranking on graph for multidimensional recommendation. In Proceedings of the 5th ACM conference on recommender systems (p. 93).Google Scholar
  22. Linden, G., Smith, B., & York, J. (2003). recommendations: Item-to-item collaborative filtering. Internet Computing, 7(1), 76–80.CrossRefGoogle Scholar
  23. Liu, H. (2010). Biosignal controlled recommendation in entertainment systems, Ph.D. dissertation, Eindhoven University of Technology.Google Scholar
  24. Lops, P., de Gemmis, M., Semeraro, G., Musto, C., & Narducci, F. (2012). Content-based and collaborative techniques for tag recommendation: an empirical evaluation. Journal of Intelligent Information Systems, 40(1), 41–61.CrossRefGoogle Scholar
  25. Manning, C.D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrival. Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  26. Mesnage, C., & Rafiq, A. (2011). Music discovery with social networks. In Proceedings of the workshop on music recommendation and discovery (pp. 1–6).Google Scholar
  27. Noguera, J.M., Barranco, M.J., Segura, R.J., & Martínez, L. (2012). A Mobile 3D-GIS hybrid recommender system for tourism a mobile 3D-GIS Hybrid recommender system for tourism. Information Sciences, 215, 37–52.CrossRefGoogle Scholar
  28. Pombinho, P., Carmo, M., & Afonso, A. (2012). Context aware point of interest adaptive recommendation. In Proceedings of the 2nd workshop on context-awareness in retrieval and recommendation (pp. 30–33).Google Scholar
  29. Qumsiyeh, R., & Ng, Y.-K. (2012). Predicting the ratings of multimedia items for making personalized recommendations. In Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval (p. 475).Google Scholar
  30. Rendle, S., Gantner, Z., Freudenthaler, C., & Schmidt-Thieme, L. (2011). Fast context-aware recommendations with factorization machines. In Proceedings of the 34th international ACM conference on research and development in information (p. 635).Google Scholar
  31. Reynolds, G., Barry, D., Burke, T., & Coyle, E. (2008). Interacting with large music collections: Towards the use of environmental metadata. In Proceedings of IEEE international conference on multimedia and expo (pp. 989–992).Google Scholar
  32. Schedl, M., Stober, S., Gomez, E., Orio, N., & Liem, C.C.S. (2012). User-aware music retrieval. In Multimodal Music Processing (vol. 3, pp. 135–156). User-Aware.Google Scholar
  33. Shi, Y., Larson, M., & Hanjalic, A. (2010). 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).Google Scholar
  34. Shi, Y., Karatzoglou, A., & Baltrunas, L. (2012). TFMAP: Optimizing MAP for top-n context-aware recommendation. In Proceedings of the 35th international ACM conference on research and development in information retrieval.Google Scholar
  35. Shi, Y., Larson, M., & Hanjalic, A. (2013). Mining contextual movie similarity with matrix factorization for context-aware recommendation. ACM Transactions on Intelligent Systems and Technology, 4(1), 1–19.CrossRefGoogle Scholar
  36. Shin, D., Lee, J.-W., Yeon, J., & Lee, S.-G. (2009). Context-aware recommendation by aggregating user context. In Proceedings of the IEEE conference on commerce and enterprise computing (pp. 423–430).Google Scholar
  37. Snedecor, G.W., & Cochran, W.G. (1980). Statistical methods, 7th edn. Ames: Iowa State University Press.zbMATHGoogle Scholar
  38. Su, J., Yeh, H., Yu, P., & Tseng, V. (2010). Music recommendation using content and context information mining. Intelligent Systems, 25(1), 16–26.CrossRefGoogle Scholar
  39. Thollot, R. (2012). Dynamic situation monitoring and context-aware BI recommendations, Ph.D. dissertation, Ecole Centrale Paris.Google Scholar
  40. Wang, X., Rosenblum, D., & Wang, Y. (2012). Context-aware mobile music recommendation for daily activities. In ACM Multimedia.Google Scholar
  41. Weng, S.-S., Lin, B., & Chen, W.-T. (2009). Using contextual information and multidimensional approach for recommendation. Expert Systems with Applications, 36, 1268–1279.CrossRefGoogle Scholar
  42. Woerndl, W., Schueller, C., Wojtech, R., & Gmbh, U. (2007). A hybrid recommender system for context-aware recommendations of mobile applications. In Proceedings of the 23rd international conference on data engineering workshop (pp. 871–878).Google Scholar
  43. Yang, W.-S., Cheng, H.-C., & Dia, J.-B. (2008). A location-aware recommender system for mobile shopping environments. Expert Systems with Applications, 34(1), 437–445.CrossRefGoogle Scholar
  44. Yazdani, A., Skodras, E., Fakotakis, N., & Ebrahimi, T. (2013). Multimedia content analysis for emotional characterization of music video clips. EURASIP Journal on Image and Video Processing, 26.Google Scholar
  45. Yu, Z., Zhou, X., Zhang, D., Chin, C.-Y., Wang, X., & Men, J. (2006). Supporting context-aware media recommendations for smart phones. Pervasive Computing, 3, 68–75.Google Scholar
  46. Yu, K., Zhang, B., & Zhu, H. (2012). Towards personalized context-aware recommendation by mining context logs. In Advances in knowledge discovery and data mining, lecture notes in computer science (vol. 7301, pp. 431–443).Google Scholar
  47. Yujie, Z., & Licai, W. (2010). Some challenges for context-aware recommender systems. In Proceedings of the 5th international conference on computer science & education (pp. 362–365).Google Scholar
  48. Zanardi, V., & Capra, L. (2008). Social ranking: uncovering relevant content using tag-based recommender systems. In Proceeding of the ACM conference on recommender systems (pp. 51–58).Google Scholar
  49. Zangerle, E., Gassler, W., & Specht, G. (2012). Exploiting Twitter’s collective knowledge for music recommendations. In Proceedings of 2nd workshop on making sense of microposts (pp. 14–17).Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

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

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