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 a variety of strong baselines on three large-scale datasets.
Original language | English (US) |
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State | Published - 2020 |
Event | 8th International Conference on Learning Representations, ICLR 2020 - Addis Ababa, Ethiopia Duration: Apr 30 2020 → … |
Conference
Conference | 8th International Conference on Learning Representations, ICLR 2020 |
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Country/Territory | Ethiopia |
City | Addis Ababa |
Period | 04/30/20 → … |
Bibliographical note
Publisher Copyright:© 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.
ASJC Scopus subject areas
- Education
- Linguistics and Language
- Language and Linguistics
- Computer Science Applications