RecSim: A Configurable Simulation Platform for Recommender Systems

2019-09-11Code Available0· sign in to hype

Eugene Ie, Chih-Wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, Craig Boutilier

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github.com/google-research/recsim
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Abstract

We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.

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Interactive Recommendation Recommendation Systems reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

RecSim: A Configurable Simulation Platform for Recommender Systems

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Abstract

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