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|Title:||Enhangcing Collaborative Filtering Music recommendation by Balancing Exploration and Exploitation||Authors:||XING ZHE||Keywords:||music recommendation, collaborative filtering, reinforcement learning, Bayesian graphical model||Issue Date:||22-Aug-2014||Citation:||XING ZHE (2014-08-22). Enhangcing Collaborative Filtering Music recommendation by Balancing Exploration and Exploitation. ScholarBank@NUS Repository.||Abstract:||Collaborative filtering (CF) techniques have shown great success in music recommendation applications. However, traditional collaborative-filtering music recommendation algorithms work in a greedy way, invariably recommending songs with the highest predicted user ratings. Such a purely exploitative strategy may result in suboptimal performance over the long term. Using a reinforcement learning approach, we introduce exploration into CF and try to strike a balance between exploration and exploitation. In order to learn users' musical tastes, we use a Bayesian graphical model that takes account of both CF latent factors and recommendation novelty. Moreover, we designed a Bayesian inference algorithm to efficiently estimate the posterior rating distributions. To the best of our knowledge, this is the first attempt to remedy the greedy nature of CF approaches in music recommendation. Results from both simulation experiments and user study show that our proposed approach significantly improves music recommendation performance.||URI:||http://scholarbank.nus.edu.sg/handle/10635/118258|
|Appears in Collections:||Master's Theses (Open)|
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