From popularity to personality: a heuristic music recommendation method for niche market

Authors:
Jun-Lin Zhou;Yan Fu;Hua Lu;Chong-Jing Sun
Affiliations:
Web Sciences Center, University of Electronic Science and Technology, Chengdu, China;Web Sciences Center, University of Electronic Science and Technology, Chengdu, China;Web Sciences Center, University of Electronic Science and Technology, Chengdu, China;Web Sciences Center, University of Electronic Science and Technology, Chengdu, China
Venue:
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Year:
2011

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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.