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Development of Reinforcement Learning Based Recommender System for Restaurants

Student: Koryagin Igor

Supervisor: Sergey Lisitsyn

Faculty: Graduate School of Business

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2021

Due to the increasing volume of data on the internet, an information overload problem becomes imperative, especially in the e-commerce domain. The recommendation system aims to tackle the issue by improving customers' experiences and increasing conversion rates for a seller. However, commonly used techniques for recommender engines such as collaborative filtering have some flaws. For instance, these approaches consider users' preferences as permanent, which does not respond to the dynamic nature of the recommendation process. This paper aims to implement and evaluate a prototype of a reinforcement learning based recommender system using a collected data set on restaurants and one of the existing deep reinforcement learning frameworks.

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