{"id":838267,"date":"2022-04-22T07:32:03","date_gmt":"2022-04-22T14:32:03","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=838267"},"modified":"2022-08-03T23:45:27","modified_gmt":"2022-08-04T06:45:27","slug":"mindsim-user-simulator-for-news-recommenders","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mindsim-user-simulator-for-news-recommenders\/","title":{"rendered":"MINDSim: User Simulator for News Recommenders"},"content":{"rendered":"

Recommender system is playing an increasingly important role in online news platforms nowadays. Recently, there is a growing demand for applying reinforcement learning (RL) algorithms to news recommendation aiming to maximize long-term and\/or non-differentiable objectives. However, without an interactive simulated environment, it is extremely costly to develop powerful RL agents for news recommendation. In this paper, we build a user simulator, namely MINDSim<\/em>, for news recommendation. Targeting at new user generation and corresponding behavior simulation, we first construct a hidden space for users using a generative adversarial network, so that new users can be generated by sampling from this hidden space. To capture complex and fast user interest drifts over time, we adopt an encoder-decoder architecture, which takes the clicked news during the simulation as input and outputs the new user interests for the next period of time. Finally, we build the MINDSim<\/em> simulator using MIcrosoft News Dataset (MIND), and extensive experimental results on this large-scale real-world dataset demonstrate that MINDSim<\/em> can simulate the behaviors of real users with high quality.<\/p>\n","protected":false},"excerpt":{"rendered":"

Recommender system is playing an increasingly important role in online news platforms nowadays. Recently, there is a growing demand for applying reinforcement learning (RL) algorithms to news recommendation aiming to maximize long-term and\/or non-differentiable objectives. However, without an interactive simulated environment, it is extremely costly to develop powerful RL agents for news recommendation. In this 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