Graph-Based Approaches for Recommender Systems

Student: Baranov Mikhail

Supervisor: Sergey Lisitsyn

Educational Programme: Big Data Systems (Master)

Final Grade: 9

Year of Graduation: 2020

Today e-commerce platforms are facing challenges to meet their customers’ needs based on huge amount of data they have collected. Recommender systems have emerged as a solution to this issue. Recommender systems makes suggestions about users’ preferences suggestions. It is majorly divided into 3 groups: content based, collaborative filtering and hybrid. But all three approaches doesn’t include interrelation between products based on their features and their historical interrelation. This work includes overview of modern approaches to recommendation tasks and deep research of application of graphs approaches, like random walk, graph convolutional neural network to recommendation task.

Full text (added May 17, 2020)

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