Exploration in Interactive Personalized Music Recommendation
作者: Xinxi WangYi WangDavid HsuYe Wang
刊名: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2014, Vol.11 (1), pp.1-22
来源数据库: Association for Computing Machinery Journal
DOI: 10.1145/2623372
关键词: Recommender systemsapplicationmachine learningmodelmusic
原始语种摘要: Current music recommender systems typically act in a greedy manner by recommending songs with the highest user ratings. Greedy recommendation, however, is suboptimal over the long term: it does not actively gather information on user preferences and fails to recommend novel songs that are potentially interesting. A successful recommender system must balance the needs to explore user preferences and to exploit this information for recommendation. This article presents a new approach to music recommendation by formulating this exploration-exploitation trade-off as a reinforcement learning task. To learn user preferences, it uses a Bayesian model that accounts for both audio content and the novelty of recommendations. A piecewise-linear approximation to the model and a variational inference...
全文获取路径: ACM  (合作)

  • formulating 公式制定
  • information 报告
  • trade 买卖
  • recommendation 建议
  • approach 
  • successful 成功的
  • proposed 建议的
  • system 
  • inference 推理
  • potentially 可能地