Welcome to RS_c, the central platform for the RecSys community. We provide curated lists of recommender-systems datasets, algorithms, books, conferences and many resources more. Maybe most importantly, we publish the latest recommender-system news. If you want your news to be reported on RS_c, read here.
HomeUtilitiesRecSim: A Configurable Simulation Platform for Recommender Systems [Google AI]
RecSim: A Configurable Simulation Platform for Recommender Systems [Google AI]
June 24, 2020
“Significant advances in machine learning, speech recognition, and language technologies are rapidly transforming the way in which recommender systems engage with users. As a result, collaborative interactive recommenders (CIRs) — recommender systems that engage in a deliberate sequence of interactions with a user to best meet that user’s needs — have emerged as a tangible goal for online services.
Despite this, the deployment of CIRs has been limited by challenges in developing algorithms and models that reflect the qualitative characteristics of sequential user interaction. Reinforcement learning (RL) is the de facto standard ML approach for addressing sequential decision problems, and as such is a natural paradigm for modeling and optimizing sequential interaction in recommender systems. However, it remains under-investigated and under-utilized for use in CIRs in both research and practice. One major impediment is the lack of general-purpose simulation platforms for sequential recommender settings, whereas simulation has been one of the primary means for developing and evaluating RL algorithms in real-world applications like robotics.
To address this, we have developed RecSim(available here), a configurable platform for authoring simulation environments to facilitate the study of RL algorithms in recommender systems (and CIRs in particular). RecSim allows both researchers and practitioners to test the limits of existing RL methods in synthetic recommender settings. RecSim’s aim is to support simulations that mirror specific aspects of user behavior found in real recommender systems and serve as a controlled environment for developing, evaluating and comparing recommender models and algorithms, especially RL systems designed for sequential user-system interaction.”