Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach
By Xinxi Wang, Yi Wang, David Hsu, Ye Wang. 2013
120 citations recorded by the source corpus.
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 algorithm help to speed up Bayesian inference. One additional benefit of our approach is a single unified model for both music recommendation and playlist generation. We demonstrate the strong potential of the proposed approach with simulation results and a user study.
Subfields: Information Systems, Management Science and Operations Research, Signal Processing
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Citations in the corpus
- DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “Our method is significantly different from the MAB group of methods [5, 7, 23, 40, 44, 50] due to its explicit modeling of future rewards, and different from previous MDP methods [27, 35, 36, 38] using user log due to its continuous representation of state and action, and the capability to scale to…”
- Explore, exploit, and explain: personalizing explainable recommendations with bandits (2018, background): “formulate interactive, personalized recommendation as a bandit task [25].”
- The use of machine learning algorithms in recommender systems: A systematic review (2015, unknown): “Zhang, 2007) Bandit 3 3 (Bouneffouf et al., 2012; Nicol et al., 2014; Wang et al., 2014) Neural Network 3 3 (Geng et al.”
- Deep Learning Based Recommender System (2017, background): “[10, 12, 113, 135, 169, 200] Review texts [11, 87, 126, 146, 174, 197, 202] ‰otes [82, 141] Images Visual features [2, 14, 25, 49, 50, 84, 98, 105, 112, 165, 172, 179, 191, 192, 197, 206] Audio Music [95, 153, 167, 168] Video Videos [14, 17, 27, 83] Networks Citation Network [9, 38, 66] Social Netwo”