Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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A Study on the Accuracy Improvement of Movie Recommender System Using Word2Vec and Ensemble Convolutional Neural Networks

Word2Vec과 앙상블 합성곱 신경망을 활용한 영화추천 시스템의 정확도 개선에 관한 연구

  • Kang, Boo-Sik (Division of Service Management, Mokwon University)
  • 강부식 (목원대학교 서비스경영학부)
  • Received : 2018.10.16
  • Accepted : 2019.01.20
  • Published : 2019.01.28
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Abstract

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.

웹 추천기법에서 가장 많이 사용하는 방식 중의 하나는 협업필터링 기법이다. 협업필터링 관련 많은 연구에서 정확도를 개선하기 위한 방안이 제시되어 왔다. 본 연구는 Word2Vec과 앙상블 합성곱 신경망을 활용한 영화추천 방안에 대해 제안한다. 먼저 사용자, 영화, 평점 정보에서 사용자 문장과 영화 문장을 구성한다. 사용자 문장과 영화 문장을 Word2Vec에 입력으로 넣어 사용자 벡터와 영화 벡터를 구한다. 사용자 벡터는 사용자 합성곱 모델에 입력하고, 영화 벡터는 영화 합성곱 모델에 입력한다. 사용자 합성곱 모델과 영화 합성곱 모델은 완전연결 신경망 모델로 연결된다. 최종적으로 완전연결 신경망의 출력 계층은 사용자 영화 평점의 예측값을 출력한다. 실험결과 전통적인 협업필터링 기법과 유사 연구에서 제안한 Word2Vec과 심층 신경망을 사용한 기법에 비해 본 연구의 제안기법이 정확도를 개선함을 알 수 있었다.

Keywords

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Fig. 1. Word2Vec skip-gram model

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Fig. 2. Convolution neural network with one convolution layer and one pooling layer

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Fig. 3. wDNN configuration

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Fig. 4. wCNN configuration

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Fig. 5. Procedure of movie recommendation algorithm

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Fig. 6. The model architecture of ensemble convolutional neural networks

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Fig. 7. The user and movie convolution model

Table 1. MAEs of 10-fold cross validation test

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Table 2. Results of pair-wise t-test of wDNN and wCNN

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