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

  • Jun-Lin ZhouEmail author
  • Yan Fu
  • Hua Lu
  • Chong-Jing Sun
Short Paper

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.

Keywords

music recommendation system niche market item popularity user personality 

Supplementary material

11390_2011_180_MOESM1_ESM.pdf (143 kb)
(PDF 143 KB)

References

  1. [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.CrossRefGoogle Scholar
  2. [2]
    Shardanand U. Social information filtering for music recommendation [Master’s Thesis]. Massachussets Institute of Technology, 1994.Google Scholar
  3. [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.Google Scholar
  4. [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.Google Scholar
  5. [5]
    Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 2003, 7(1): 76–81.CrossRefGoogle Scholar
  6. [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.Google Scholar
  7. [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.Google Scholar
  8. [8]
    Foote J. Content-based retrieval of music and audio. Multimedia Storage and Archiving Systems II. In Proc. SPIE, 1997, pp.138-147.Google Scholar
  9. [9]
    Tzanetakis G. Manipulation, Analysis and Retrieval Systems for Audio Signals. Princeton, NJ, USA: Princeton University, 2002.Google Scholar
  10. [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.Google Scholar
  11. [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.CrossRefGoogle Scholar
  12. [12]
    Zhou T, Ren J, Medo M, Zhang Y C. Bipartite network projection and personal recommendation. Physical Review E, 2007, 76(4): 046115.CrossRefGoogle Scholar
  13. [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.CrossRefGoogle Scholar
  14. [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. [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.CrossRefGoogle Scholar
  16. [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.Google Scholar
  17. [17]
    Billsus D, Pazzani M J. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 2000, 10(2/3): 147–180.CrossRefGoogle Scholar
  18. [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.CrossRefGoogle Scholar
  19. [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. [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.CrossRefGoogle Scholar
  21. [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.CrossRefGoogle Scholar
  22. [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.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC & Science Press, China 2011

Authors and Affiliations

  1. 1.Web Sciences CenterUniversity of Electronic Science and TechnologyChengduChina

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