Skip to main content
SpringerLink
Log in

From Popularity to Personality — A Heuristic Music Recommendation Method for Niche Market

  • Short Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

In most current recommender systems, the goal to accurately predict what people want leads to the tendency to recommend popular items, which is less helpful in revealing user’s personality, especially to new users. In this paper, we propose a heuristic music recommendation method for niche market by focusing on how to identify user’s personality as soon as possible. Instead of trying to improve algorithm's performance on new users by recommending the most popular items, we work on how to make them “familiar” with the system earlier. The method is more suitable for brand-new users, and gives a hint to solve the cold start problem. In real applications it is better to combine it with a traditional approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Goldberg D, Nichols D, Oki B M, Terry D. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 1992, 35(12): 61–70.

    Article  Google Scholar 

  2. Shardanand U. Social information filtering for music recommendation [Master’s Thesis]. Massachussets Institute of Technology, 1994.

  3. Shardanand U, Maes P. Social information filtering: Algorithms for automaing “word of mouth”. In Proc. ACM CHI 1995, Denver, USA, May 7–11, 1995, pp.210-217.

  4. Resnick P, Iacovou N, Suchak M, Bergstorm P, Riedl J. Grou-plens: An open architecture for collaborative filtering of net-news. In Proc. ACM 1994 Conference on Computer Supported Cooperative Work, Chapel Hill, USA, Oct. 22–26, 1994, pp.175-186.

  5. Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 2003, 7(1): 76–81.

    Article  Google Scholar 

  6. Schein A I, Popescul A, Ungar L H, Pennock D M. Generative models for cold-start recommendations. In Proc. 2001 SIGIR Workshop Recomm. Syst., New Orleans, USA, Sept. 9–13, 2001, pp.141-149.

  7. Schein A I, Popescul A, Ungar L H, Pennock D M. Methods and metrics for cold-start recommendations. In Proc. the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002), Tampere, Finland, Aug. 11–15, 2002, pp.253-260.

  8. Foote J. Content-based retrieval of music and audio. Multimedia Storage and Archiving Systems II. In Proc. SPIE, 1997, pp.138-147.

  9. Tzanetakis G. Manipulation, Analysis and Retrieval Systems for Audio Signals. Princeton, NJ, USA: Princeton University, 2002.

    Google Scholar 

  10. Cataltepe Z, Altinel B. Music recommendation based on adaptive feature and user grouping. In Proc. 22nd International Symposium on Computer and Information Sciences, Ankara, Turkey, Nov. 7–9, 2007, pp.1-6.

  11. Yoshii K, Goto M, Komatani K, Ogata T, Okuno H G. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. IEEE Transaction on Audio Speech and Language Processing, 2008, 16(2): 435–447.

    Article  Google Scholar 

  12. Zhou T, Ren J, Medo M, Zhang Y C. Bipartite network projection and personal recommendation. Physical Review E, 2007, 76(4): 046115.

    Article  Google Scholar 

  13. Zhou T, Jiang L L, Su R Q, Zhang Y C. Effect of initial configuration on network-based recommendation. Europhys. Lett., 2008, 81(5): 58004.

    Article  Google Scholar 

  14. Huang Z, Chen H, Zeng D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. IEEE Trans. Inf. Syst., 2004, 22(1): 116–142.

    Google Scholar 

  15. Huang Z, Zeng D, Chen H. Analyzing consumer-product graphs: Empirical ¯ndings and applications in recommender systems. Management Science, 2007, 53(7): 1146–1164.

    Article  Google Scholar 

  16. Weng L T, Xu Y, Li Y, Nayak R. Improving recommendation novelty based on topic taxonomy. In Proc. the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Washington DC, USA, Nov. 2–5, 2007, pp.115–118.

  17. Billsus D, Pazzani M J. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 2000, 10(2/3): 147–180.

    Article  Google Scholar 

  18. Zhou T, Kuscsik Z, Liu J G, Medo M, Wakeling J, Zhang Y C. Solving the apparent diversity-accuracy dilemma of recommender systems. In Proc. the National Academy of Sciences, 2010, 107(10): pp.4511-4515.

    Article  Google Scholar 

  19. Zhang Z K, Liu C. A hypergraph model of social tagging networks. J. Stat. Mech., 2010, P10005, doi: 10.1088/1742-5468/2010/10/P10005.

  20. Shang M S, Zhang Z K. Diffusion-based recommendation in collaborative tagging systems. Chinese Phys. Lett., 2009, 26: 118903 doi: 10.1088/0256-307X/26/11/118903.

    Article  Google Scholar 

  21. Zhang Z K, Zhou T, Zhang Y C. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A: Statistical Mechanics and Its Applications, 2010, 389(1): 179–186.

    Article  Google Scholar 

  22. Zhang Z K, Liu C, Zhang Y C, Zhou T. Solving the cold-start problem in recommender systems with social tags. Europhysics Letters, 2010, 92(2): 28002.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Web Sciences Center, University of Electronic Science and Technology, Chengdu, 610054, China

    Jun-Lin Zhou, Yan Fu, Hua Lu & Chong-Jing Sun

Authors
  1. Jun-Lin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hua Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chong-Jing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun-Lin Zhou.

Additional information

This work is supported by the National Natural Science Foundation of China under Grant Nos. 60973120, 60903073, 61003231, 61103109, and 11105024.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

(PDF 143 KB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhou, JL., Fu, Y., Lu, H. et al. From Popularity to Personality — A Heuristic Music Recommendation Method for Niche Market. J. Comput. Sci. Technol. 26, 816–822 (2011). https://doi.org/10.1007/s11390-011-0180-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-011-0180-5

Keywords

Search

Navigation