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Towards context-sensitive collaborative media recommender system

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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.

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Notes

  1. The MovieLens dataset can be downloaded from: http://www.grouplens.org/node/73.

References

  1. Al Osman H, Eid M, and El Saddik A (2013) U-biofeedback: a multimedia-based reference model for ubiquitous biofeedback systems. Multimed. Tools Appl

  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. ACM

  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):1462–9

    Article  Google Scholar 

  4. Bogers T (2010) Movie recommendation using random walks over the contextual graph. In Proc. of the 2nd Intl. Workshop on Context-Aware Recommender Systems

  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. 43–52

  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. 553–556

  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):1043–1065

    Article  Google Scholar 

  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. 475–478

  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):785–799

    Article  Google Scholar 

  10. Deshpande M, Karypis G (2004) Item-based top- N recommendation algorithms. ACM Trans Inf Syst 22(1):143–177

    Article  Google Scholar 

  11. Egelund N (1982) Spectral analysis of heart rate variability as an indicator of driver fatigue. Ergonomics 25(7):663–72

    Article  Google Scholar 

  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 2766–2771

  13. Han B, Rho S, Jun S, Hwang E (2009) Music emotion classification and context-based music recommendation. Multimed Tools Appl 47(3):433–460

    Article  Google Scholar 

  14. Healey JA, Picard RW (2005) Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transp Syst 6(2):156–166

    Article  Google Scholar 

  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. ACM

  16. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

    Article  Google Scholar 

  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):84–9

    Article  Google Scholar 

  18. Hyung Z, Lee K, and Lee K (2013) Music recommendation using text analysis on song requests to radio stations. Expert Syst. Appl, no. October

  19. Jäschke R, Marinho L, Hotho A, Schmidt-Thieme L, Stumme G (2008) Tag recommendations in social bookmarking systems. AI Commun 21(4):231–247

    MATH  MathSciNet  Google Scholar 

  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):855–872

  21. Lattimore PJ (2001) Stress-induced eating: an alternative method for inducing ego-threatening stress. Appetite 36(2):187–8

    Article  Google Scholar 

  22. Lekakos G, Caravelas P (2006) A hybrid approach for movie recommendation. Multimed Tools Appl 36(1–2):55–70

    Google Scholar 

  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. ACM

  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):41–61

    Article  Google Scholar 

  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):40–4

    Article  Google Scholar 

  26. Nasoz F, Lisetti CL, Vasilakos AV (2010) Affectively intelligent and adaptive car interfaces. Inf Sci (Ny) 180(20):3817–3836

    Article  Google Scholar 

  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, Sensys

  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)

  29. Pessemier T, Dooms S, and Martens L (2013) Context-aware recommendations through context and activity recognition in a mobile environment. Multimed. Tools Appl., Jul

  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 2007:4656–9

    Google Scholar 

  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 423–430

  32. Siska E (2002) The stroop colour-word test in psychology and biomedicine. Univ Palacki Olomuc GYMNICA 32(1):45–52

    Google Scholar 

  33. Su J, Yeh H, Yu P, Tseng V (2010) Music recommendation using content and context information mining. Intell Syst IEEE 25(1):16–26

    Article  Google Scholar 

  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):9–15

    Article  Google Scholar 

  35. van Rijsbergen CJ (1979) Information retrieval, 2nd edn. Butterworths, London

    Google Scholar 

  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. 871–878

  37. Yeung KF (2011) A context-aware framework for personalized recommendation in mobile environments. University of Portsmouth, Portsmouth, United Kngdom

    Google Scholar 

  38. Yoon K, Lee J, Kim M (2012) Music recommendation system using emotion triggering low-level features. Consum Electron IEEE Trans 58(2):612–618

    Article  MathSciNet  Google Scholar 

  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 7301:431–443

    Article  Google Scholar 

  40. Yujie Z and Licai W (2010) Some challenges for context-aware recommender systems. 2010 5th. Int Conf Comput Sci Educ, pp. 362–365

  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 51–58

  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. 14–17

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Authors and Affiliations

  1. Multimedia Communications Research Laboratory (MCRlab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Mohammed F. Alhamid, Hussein Al Osman & Abdulmotaleb El Saddik

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

    Mohammed F. Alhamid, M. Shamim Hossain & Abdulmotaleb El Saddik

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

    Majdi Rawashdeh

Authors
  1. Mohammed F. Alhamid
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  2. Majdi Rawashdeh
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  3. Hussein Al Osman
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  4. M. Shamim Hossain
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  5. Abdulmotaleb El Saddik
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Correspondence to Mohammed F. Alhamid.

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Alhamid, M.F., Rawashdeh, M., Al Osman, H. et al. Towards context-sensitive collaborative media recommender system. Multimed Tools Appl 74, 11399–11428 (2015). https://doi.org/10.1007/s11042-014-2236-3

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  • DOI: https://doi.org/10.1007/s11042-014-2236-3

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