Multimedia Tools and Applications

, Volume 59, Issue 1, pp 241–258 | Cite as

A Cascade-Hybrid Music Recommender System for mobile services based on musical genre classification and personality diagnosis

  • Aristomenis S. Lampropoulos
  • Paraskevi S. Lampropoulou
  • George A. Tsihrintzis


In this paper, we present a Cascade-Hybrid Music Recommender System intended to operate as a mobile service. Specifically, our system is a middleware that realizes the recommendation process based on a combination of music genre classification and personality diagnosis. A mobile user is able to query for music files by simply sending an example music file from his/her mobile device. In response to the user query, the system recommends music files that not only belong to the same genre as the user query, but also an attempt has been made to take into account both the user preferences as well as ratings from other users for candidate results. The recommendation mechanism is realized by applying the collaborative filtering technique of personality diagnosis. Using the minimum absolute error and the ranked scoring criteria, our approach is compared to existing recommendation techniques that rely on either collaborative filtering or content-based approaches. The outcome of the comparison clearly indicates that our approach exhibits significantly higher performance.


Recommender system Cascade-Hybrid method Personality diagnosis Mobile services 


  1. 1.
    Adomavicius G, Tuzhilin E (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17:734–749CrossRefGoogle Scholar
  2. 2.
    Albanese M, Chianese A, d’Acierno A, Moscato V, Picariello A (2010) A multimedia recommender integrating object features and user behavior. Multimed Tools Appl (MTAP) 50:563–585CrossRefGoogle Scholar
  3. 3.
    Billsus D, Pazzani MJ (2000) User modeling for adaptive news access. User Model User-Adapt Interact 10(2–3):147–180. doi: 10.1023/A:1026501525781 CrossRefGoogle Scholar
  4. 4.
    Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. fourteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann, Los Altos, pp 43–52Google Scholar
  5. 5.
    Burke R (2000) Knowledge-based recommender systemsGoogle Scholar
  6. 6.
    Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12(4):331–370. doi: 10.1023/A:1021240730564 MATHCrossRefGoogle Scholar
  7. 7.
    Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content-based and collaborative filters in an online newspaper. In: Proc. ACM SIGIR workshop on recommender systemsGoogle Scholar
  8. 8.
    Cohen WW, Fan W (2000) Web-collaborative filtering: recommending music by crawling the Web. Comput Netw (Amsterdam, Netherlands: 1999) 33(1–6):685–698CrossRefGoogle Scholar
  9. 9.
    Han B, Rhon S, Jun S, Hwang E (2010) Music emotion classification and context-based music recommendation. Multimed Tools Appl (MTAP) 47(3):433–460CrossRefGoogle Scholar
  10. 10.
    Hossian M, Parra J, Atrey P, Saddik A (2010) A framework for human-centered provisioning of ambient media services. Multimed Tools Appl (MTAP) 43(3):407–431Google Scholar
  11. 11.
    Kannel: open source WAP and SMS gateway. URL:
  12. 12.
    Kreßel UHG (1999) Pairwise classification and support vector machines. MIT Press, Cambridge, pp 255–268Google Scholar
  13. 13.
    Lampropoulou PS, Lampropoulos AS, Tsihrintzis GA (2006) Alimos: a middleware system for accessing digital music libraries in mobile services. In: Proc. KES2006 10th international conference on knowledge-based and intelligent information and engineering systems. Bournemouth, UKGoogle Scholar
  14. 14.
    Lampropoulou PS, Lampropoulos AS, Tsihrintzis GA (2008) Evaluation of a middleware system for accessing digital music libraries in mobile services. In: 5th international conference on information technology: new generations. Las Vegas, NV, USAGoogle Scholar
  15. 15.
    Lampropoulos P, Lampropoulos A, Tsihrintzis A (2009) Intelligent mobile content-based retrieval from digital music libraries. Intell Decis Technol 3(3): 123–138Google Scholar
  16. 16.
    Nokia: Series 40 developer platform 2.0 SDK. URL:
  17. 17.
    Paolo M, Paolo A (2007) Trust-aware recommender systems. In: RecSys ’07: proceedings of the 2007 ACM conference on recommender systems, pp 17–24Google Scholar
  18. 18.
    Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting Web sites. Mach Learn 27(3):313–331. doi: 10.1023/A:1007369909943 CrossRefGoogle Scholar
  19. 19.
    Pennock DM, Horvitz E, Lawrence S, Giles CL (2000) Collaborative filtering by personality diagnosis: a hybrid memory and model-based approach. In: UAI ’00: proceedings of the 16th conference on uncertainty in artificial intelligence. Morgan Kaufmann, San Francisco, pp 473–480Google Scholar
  20. 20.
    Push access protocol specification, wap-247-pap-20010429-a, wap forum. URL: (2001)
  21. 21.
    Push architectural overview, wap-250-pusharchoverview-20010703-p, wap forum. URL: (2001)
  22. 22.
    Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3):56–57CrossRefGoogle Scholar
  23. 23.
    Rojsattarat E, Soonthornphisaj N (2003) Hybrid recommendation: combining content-based prediction and collaborative filtering, pp 337–344Google Scholar
  24. 24.
    Rudstrom A, Svensson M, Coster R, Hook K (2004) Mobitip: using bluetooth as a mediator of social context. In: In Ubicomp 2004 adjunct proceedingsGoogle Scholar
  25. 25.
    Smyth B, Cotter P (2000) A personalized tv listings service for the digital tv age. Knowl-Based Syst 13:53–59CrossRefGoogle Scholar
  26. 26.
    Tosi D (2003) An advanced architecture for push services. In: Proceedings of the fourth international conference on Web information systems engineering workshops (WISEW’03), pp 193–200Google Scholar
  27. 27.
    Tran T, Cohen R (2000) Hybrid recommender systems for electronic commerce. In: Proc. in knowledge-based electronic markets, papers from the AAAI workshop. AAAI Technical Report WS-00-04, Menlo Park, CA, pp 78–83Google Scholar
  28. 28.
    Tzanetakis G, Cook P (2000) Marsyas: a framework for audio analysis. Organ Sound 4(3):169–175CrossRefGoogle Scholar
  29. 29.
    Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10(5):293–302CrossRefGoogle Scholar
  30. 30.
    Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999CrossRefGoogle Scholar
  31. 31.
    Yoon WJ, Oh SH, Park KS (2006) Robust music information retrieval on mobile network based on multi-feature clustering. In: Li X, Zaiäne OR, Li Z (eds) The second international conference on advanced data mining and applications (ADMA 2006). Lecture Notes in Computer Science, vol 4093. Springer, New York, pp 279–283Google Scholar
  32. 32.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Aristomenis S. Lampropoulos
    • 1
  • Paraskevi S. Lampropoulou
    • 1
  • George A. Tsihrintzis
    • 1
  1. 1.Department of InformaticsUniversity of PiraeusPiraeusGreece

Personalised recommendations