TY - JOUR

T1 - Two tower collaborative filtering algorithm for movie recommendation

AU - Zhao, Chi

A2 - Blekanov, Ivan

N1 - Conference code: CPS'21

PY - 2021

Y1 - 2021

N2 - In this paper, authors mainly focus on representing users and movies as vectors in a better way. This step is essential for a recommendation system, which determines the performance of the recommendation system. Through the Deep Learning technique, by combining the traditional Collaborative Filtering algorithm with matrix factorization algorithm, we propose a new model architecture based on the NeuralCF model\cite{he2017neural} and Youtube Two Tower model, which we call the "Two Tower NeuralCF model". The model was tested on the Movielens 100k and 1M datasets and obtained better results than the NeuralCF model.

AB - In this paper, authors mainly focus on representing users and movies as vectors in a better way. This step is essential for a recommendation system, which determines the performance of the recommendation system. Through the Deep Learning technique, by combining the traditional Collaborative Filtering algorithm with matrix factorization algorithm, we propose a new model architecture based on the NeuralCF model\cite{he2017neural} and Youtube Two Tower model, which we call the "Two Tower NeuralCF model". The model was tested on the Movielens 100k and 1M datasets and obtained better results than the NeuralCF model.

UR - https://www.elibrary.ru/item.asp?id=46227006

UR - https://proxy.library.spbu.ru:2228/item.asp?id=46227006

M3 - Article

VL - 8

SP - 397

EP - 401

JO - Процессы управления и устойчивость

JF - Процессы управления и устойчивость

SN - 2313-7304

IS - 1

T2 - Control Processes and Stability (CPS-21)

Y2 - 5 April 2021 through 9 April 2021

ER -