[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.13160/ricns.2021.14.4.183

Modeling of Convolutional Neural Network-based Recommendation System

Kim, Tae-Yeun (National Program of Excellence in Software center, Chosun University)

Publication Information

Journal of Integrative Natural Science / v.14, no.4, 2021 , pp. 183-188 More about this Journal

Abstract

Collaborative filtering is one of the commonly used methods in the web recommendation system. Numerous researches on the collaborative filtering proposed the numbers of measures for enhancing the accuracy. This study suggests the movie recommendation system applied with Word2Vec and ensemble convolutional neural networks. First, user sentences and movie sentences are made from the user, movie, and rating information. Then, the user sentences and movie sentences are input into Word2Vec to figure out the user vector and movie vector. The user vector is input on the user convolutional model while the movie vector is input on the movie convolutional model. These user and movie convolutional models are connected to the fully-connected neural network model. Ultimately, the output layer of the fully-connected neural network model outputs the forecasts for user, movie, and rating. The test result showed that the system proposed in this study showed higher accuracy than the conventional cooperative filtering system and Word2Vec and deep neural network-based system suggested in the similar researches. The Word2Vec and deep neural network-based recommendation system is expected to help in enhancing the satisfaction while considering about the characteristics of users.

Keywords

Modeling of Convolutional; Neural Network-based; Recommendation System;

Citations & Related Records

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