NetEase Fuxi & Ray Summit 2023: Exploring the Innovation Road of Reinforcement Learning Recommendation System

Recently, the second Ray Summit conference was successfully held in San Francisco, USA. As the top international big data technology summit, Ray Summit is dedicated to displaying and discussing best practices for building and expanding artificial intelligence applications and infrastructure using the Ray framework, aiming to promote innovation and exchange in the fields of artificial intelligence, machine learning and distributed computing. , thousands of engineers, scholars and industry experts from DeepMind, OpenAI, Uber, LinkedIn, Niantic and other companies and institutions participate every year. NetEase Fuxi, as a cutting-edge team in the field of domestic artificial intelligence, was also invited to participate in this meeting.

In order to verify the effectiveness of RL4RS, NetEase Fuxi has implemented practical applications in its multiple game businesses. By using the reinforcement learning recommendation system built by RL4RS, player behavior is learned and optimized, thereby improving user satisfaction of the game and providing support for the smooth operation of the game system. The success of this application not only proves the feasibility of RL4RS, but also opens up a new direction for recommendation system technology.

Dr. Wu also introduced the evaluation framework of RL4RS, which can not only comprehensively evaluate the performance of recommendation systems, but also help researchers better understand and analyze the advantages and disadvantages of recommendation algorithms. at. The introduction of this framework fills the gap in the field of recommendation system evaluation and provides important support for the research and application of recommendation algorithms. The introduction of this evaluation framework provides a comprehensive and systematic method for the performance evaluation of recommendation systems. Through this framework, researchers can evaluate the performance of recommendation systems in different scenarios and user groups, and conduct more in-depth analysis of recommendation algorithms. In this way, researchers can better understand the advantages of recommendation systems and

Dr. Runze’s speech aroused enthusiastic responses at the scene, allowing the audience to have a deeper understanding of the RL4RS project importance and potential, and also demonstrates the infinite vitality in the field of recommendation systems. We look forward to having more people who are passionate about reinforcement learning join us in the future, injecting new vitality into technological innovation and the development of artificial intelligence.

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