Multimedia Tools and Applications

, Volume 74, Issue 24, pp 11399–11428 | Cite as

Towards context-sensitive collaborative media recommender system

  • Mohammed F. Alhamid
  • Majdi Rawashdeh
  • Hussein Al Osman
  • M. Shamim Hossain
  • Abdulmotaleb El Saddik
Article
First Online:
Received:
Revised:
Accepted:

Abstract

With the rapid increase of social media resources and services, Internet users are overwhelmed by the vast quantity of social media available. Most recommender systems personalize multimedia content to the users by analyzing two main dimensions of input: content (item), and user (consumer). In this study, we address the issue of how to improve the recommendation and the quality of the user experience by analyzing the contextual aspect of the users, at the time when they wish to consume multimedia content. Mainly, we highlight the potential of including a user’s biological signal and leveraging it within an adapted collaborative filtering algorithm. First, the proposed model utilizes existing online social networks by incorporating social tags and rating information in ways that personalize the search for content in a particular detected context. Second, we propose a recommendation algorithm to improve the user experience and satisfaction with the use of a biosignal in the recommendation process. Our experimental results show the feasibility of personalizing the recommendation according to the user’s context, and demonstrate some improvement on cold start situations where relatively little information is known about a user or an item.

Keywords

Personalized search Context media search Context-aware recommendation Collaborative context Retrieval model Information filtering 

References

  1. 1.
    Al Osman H, Eid M, and El Saddik A (2013) U-biofeedback: a multimedia-based reference model for ubiquitous biofeedback systems. Multimed. Tools ApplGoogle Scholar
  2. 2.
    Alhamid M F, Rawashdeh M, Osman H Al, and 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. ACMGoogle Scholar
  3. 3.
    Bernardi L, Wdowczyk-Szulc J, Valenti C, Castoldi S, Passino C, Spadacini G, Sleight P (2000) Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability. J Am Coll Cardiol 35(6):1462–9CrossRefGoogle Scholar
  4. 4.
    Bogers T (2010) Movie recommendation using random walks over the contextual graph. In Proc. of the 2nd Intl. Workshop on Context-Aware Recommender SystemsGoogle Scholar
  5. 5.
    Breese J, Heckerman D, and Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proc. Fourteenth Annu. Conf. Uncertain. Artifcial Intell, pp. 43–52Google Scholar
  6. 6.
    Cai R, Zhang C, Wang C, Zhang L, and Ma W (2007) MusicSense: Contextual Music Recommendation using Emotional Allocation Modeling. In Proc. MULTIMEDIA’07 Proceedings of the 15th international conference on Multimedia, pp. 553–556Google Scholar
  7. 7.
    Camm DHSAJ, Malik M, Bigger JT, Breithardt G, Cerutti S, Cohen RJ, Coumel P, Fallen EL, Kennedy HL, Kleiger RE, Lombardi F, Malliani A, Moss AJ, Rottman JN, Schmidt G, Schwartz PJ (1996) Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task force of the European society of cardiology and the North American society of pacing and electrophysiology. Circulation 93(5):1043–1065CrossRefGoogle Scholar
  8. 8.
    Colombo R, Mazzuero G, Sofffiantino F, Ardizzoia M, Minuco G (1989) A comprehensive PC solution to heart rate variability analysis in mental stress. Comput. Cardiol. 1989, Proceedings. IEEE, Jerusalem, pp. 475–478Google Scholar
  9. 9.
    de Campos LM, Fernández-Luna JM, Huete JF, Rueda-Morales MA (2010) Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks. Int J Approx Reason 51(7):785–799CrossRefGoogle Scholar
  10. 10.
    Deshpande M, Karypis G (2004) Item-based top- N recommendation algorithms. ACM Trans Inf Syst 22(1):143–177CrossRefGoogle Scholar
  11. 11.
    Egelund N (1982) Spectral analysis of heart rate variability as an indicator of driver fatigue. Ergonomics 25(7):663–72CrossRefGoogle Scholar
  12. 12.
    Gori M, Pucci A, Roma V, and Siena I (2007) Item rank: A random-walk based scoring algorithm for recommender engines. In the 20th international joint conference on Artificial Intelligence, pp 2766–2771Google Scholar
  13. 13.
    Han B, Rho S, Jun S, Hwang E (2009) Music emotion classification and context-based music recommendation. Multimed Tools Appl 47(3):433–460CrossRefGoogle Scholar
  14. 14.
    Healey JA, Picard RW (2005) Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transp Syst 6(2):156–166CrossRefGoogle Scholar
  15. 15.
    Herlocker J and Konstan J (1999) An algorithmic framework for performing collaborative filtering. Proc. 22nd Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. retrieval. ACMGoogle Scholar
  16. 16.
    Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53CrossRefGoogle Scholar
  17. 17.
    Hjortskov N, Rissén D, Blangsted AK, Fallentin N, Lundberg U, Søgaard K (2004) The effect of mental stress on heart rate variability and blood pressure during computer work. Eur J Appl Physiol 92(1–2):84–9CrossRefGoogle Scholar
  18. 18.
    Hyung Z, Lee K, and Lee K (2013) Music recommendation using text analysis on song requests to radio stations. Expert Syst. Appl, no. OctoberGoogle Scholar
  19. 19.
    Jäschke R, Marinho L, Hotho A, Schmidt-Thieme L, Stumme G (2008) Tag recommendations in social bookmarking systems. AI Commun 21(4):231–247MATHMathSciNetGoogle Scholar
  20. 20.
    Kim J, Lee D, and Chung K.-Y (2014) Item recommendation based on context-aware model for personalized u-healthcare service. Multimed Tools Appl 71(2):855–872Google Scholar
  21. 21.
    Lattimore PJ (2001) Stress-induced eating: an alternative method for inducing ego-threatening stress. Appetite 36(2):187–8CrossRefGoogle Scholar
  22. 22.
    Lekakos G, Caravelas P (2006) A hybrid approach for movie recommendation. Multimed Tools Appl 36(1–2):55–70Google Scholar
  23. 23.
    Liu H, Hu J, and Rauterberg M (2010) iHeartrate: a heart rate controlled in-flight music recommendation system. Proc. 7th Int Conf Methods Tech. Behav. Res. ACMGoogle Scholar
  24. 24.
    Lops P, de Gemmis M, Semeraro G, Musto C, Narducci F (2012) Content-based and collaborative techniques for tag recommendation: an empirical evaluation. J Intell Inf Syst 40(1):41–61CrossRefGoogle Scholar
  25. 25.
    Narducci F, Snape WJ, Battle WM, London RL, Cohen S (1985) Increased colonic motility during exposure to a stressful situation. Dig Dis Sci 30(1):40–4CrossRefGoogle Scholar
  26. 26.
    Nasoz F, Lisetti CL, Vasilakos AV (2010) Affectively intelligent and adaptive car interfaces. Inf Sci (Ny) 180(20):3817–3836CrossRefGoogle Scholar
  27. 27.
    Nirjon S, Dickerson R, Li Q, Asare P, Stankovic J, Hong D, Zhang B, Shen G, Jiang X, and Zhao F (2012) MusicalHeart: A Hearty Way of Listening to Music. ACM, SensysGoogle Scholar
  28. 28.
    Oliver N and Flores-Mangas F (2006) MPTrain: a mobile, music and physiology-based personal trainer. Proc. 8th Conf. Human-Comput. Interact. Mob. Devices Serv. (Mobile HCI 2006)Google Scholar
  29. 29.
    Pessemier T, Dooms S, and Martens L (2013) Context-aware recommendations through context and activity recognition in a mobile environment. Multimed. Tools Appl., JulGoogle Scholar
  30. 30.
    Salahuddin L, Cho J, Jeong MG, Kim D (2007) Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings. Conf Proc IEEE Eng Med Biol Soc 2007:4656–9Google Scholar
  31. 31.
    Shin D, Lee J, Yeon J, and Lee S (2009) Context-Aware Recommendation by Aggregating User Context. Commerce and Enterprise Computing, 2009. CEC'09. IEEE, Vienna, pp 423–430Google Scholar
  32. 32.
    Siska E (2002) The stroop colour-word test in psychology and biomedicine. Univ Palacki Olomuc GYMNICA 32(1):45–52Google Scholar
  33. 33.
    Su J, Yeh H, Yu P, Tseng V (2010) Music recommendation using content and context information mining. Intell Syst IEEE 25(1):16–26CrossRefGoogle Scholar
  34. 34.
    Tulen JH, Moleman P, van Steenis HG, Boomsma F (1989) Characterization of stress reactions to the stroop color word test. Pharmacol Biochem Behav 32(1):9–15CrossRefGoogle Scholar
  35. 35.
    van Rijsbergen CJ (1979) Information retrieval, 2nd edn. Butterworths, LondonGoogle Scholar
  36. 36.
    Woerndl W, Schueller C, Wojtech R, and Gmbh U (2007) A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications, Data Eng. Work. 2007 I.E. 23rd. Int Conf on, pp. 871–878Google Scholar
  37. 37.
    Yeung KF (2011) A context-aware framework for personalized recommendation in mobile environments. University of Portsmouth, Portsmouth, United KngdomGoogle Scholar
  38. 38.
    Yoon K, Lee J, Kim M (2012) Music recommendation system using emotion triggering low-level features. Consum Electron IEEE Trans 58(2):612–618MathSciNetCrossRefGoogle Scholar
  39. 39.
    Yu K, Zhang B, Zhu H (2012) Towards Personalized Context-Aware Recommendation by Mining Context Logs. Adv Knowl Discov Data Mining, Lect Notes Comput Sci 7301:431–443CrossRefGoogle Scholar
  40. 40.
    Yujie Z and Licai W (2010) Some challenges for context-aware recommender systems. 2010 5th. Int Conf Comput Sci Educ, pp. 362–365Google Scholar
  41. 41.
    Zanardi V and Capra L (2008) Social ranking: uncovering relevant content using tag-based recommender systems. In Proceeding of 2008 ACM conference on Recommender systems, pp 51–58Google Scholar
  42. 42.
    Zangerle E, Gassler W, and Specht G (2012) Exploiting Twitter’s Collective Knowledge for Music Recommendations. Proc. WWW Work. #MSM2012 (2nd Work. Mak. Sense Microposts), pp. 14–17Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

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

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