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

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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.

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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
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  2. Yan Fu
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  3. Hua Lu
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  4. Chong-Jing Sun
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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.

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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

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

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