Reinforcement Learning is the science of learning what to do and how to do it by introducing a reward-penalty system. The reward function is applied whenever an ‘agent’ wins the ‘game’ over a certain number of rounds. The agent is a program that processes a sequence of decisions in an unexplored environment to achieve some goals. With trial and error, the agent’s objective is to learn to make decisions that will result in a maximum reward.
Silicon Orchard Research and Analytics team has extended its research on Reinforcement Learning to devise an intelligent way to capture human interactions with the Internet. The team introduces a novel approach to utilize reinforcement learning algorithms that understand human preferences and can devise recommendation systems without the need of collecting any identifiable user data.
We are proud to present our research Leveraging Reinforcement Learning to build a Recommendation System for Incognito mode Users has been published in the ICML 2021 Workshop on Representation Learning for Finance and e-Commerce Applications, part of the prestigious A* conference, International Conference on Machine Learning (ICML).
Abstract: In this research, we propose novel reinforcement learning-based algorithms to recommend users without collecting identifiable data. With just only user activity on a session, our algorithm can model and track user behavior and formulate a recommendation system. We conclude our algorithms demonstrate positive results in capturing user behavior without collecting private data of any kind from the user.