RecSim：一个可配置的推荐系统仿真平台
RecSim: A Configurable Simulation Platform for Recommender Systems
摘要：
我们提出RecSim，一个可配置的平台，创作的推荐系统（RS），自然支持与用户的顺序交互的模拟环境。RecSim允许创建新的环境，反映用户行为和项目结构的特定方面，在一个抽象的水平，非常适合推动当前的强化学习（RL）和RS技术在顺序交互式推荐问题的限制。环境可以很容易地配置，以改变以下假设：用户偏好和项目熟悉度;用户潜在状态及其动态;选择模型和其他用户响应行为。我们概述了RecSim如何为RL和RS研究人员和从业者提供价值，以及它如何作为学术-工业合作的工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
