Toward Amortized Ranking-Critical Training For Collaborative Filtering

Anonymous

Sep 25, 2019 ICLR 2020 Conference Blind Submission readers: everyone Show Bibtex
  • Keywords: Collaborative Filtering, Recommender Systems, Actor-Critic, Learned Metrics
  • TL;DR: We apply the actor-critic methodology from reinforcement learning to collaborative filtering, resulting in improved performance across a variety of latent-variable models
  • Abstract: We investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to more directly maximize ranking-based objective functions. Specifically, we train a critic network to approximate ranking-based metrics, and then update the actor network to directly optimize against the learned metrics. In contrast to traditional learning-to-rank methods that require re-running the optimization procedure for new lists, our critic-based method amortizes the scoring process with a neural network, and can directly provide the (approximate) ranking scores for new lists. We demonstrate the actor-critic's ability to significantly improve the performance of a variety of prediction models, and achieve better or comparable performance to the state-of-the-art on three large-scale datasets.
  • Code: https://github.com/RACT-CF/RaCT
0 Replies

Loading