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Performance Evaluation of Recurrent Neural Network Algorithms for Recommendation System in E-commerce
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전자상거래 추천시스템을 위한 순환신경망 알고리즘들의 성능평가

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Type
Academic journal
Author
Jihye Seo (이화여자대학교) Hwan-Seung Yong (이화여자대학교)
Journal
Korean Institute of Information Scientists and Engineers KIISE Transactions on Computing Practices Vol.23 No.7 KCI Accredited Journals
Published
2017.7
Pages
440 - 445 (6page)
DOI
10.5626/KTCP.2017.23.7.440

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Performance Evaluation of Recurrent Neural Network Algorithms for Recommendation System in E-commerce
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Due to the advance of e-commerce systems, the number of people using online shopping and products has significantly increased. Therefore, the need for an accurate recommendation system is becoming increasingly more important. Recurrent neural network is a deep-learning algorithm that utilizes sequential information in training. In this paper, an evaluation is performed on the application of recurrent neural networks to recommendation systems. We evaluated three recurrent algorithms (RNN, LSTM and GRU) and three optimal algorithms(Adagrad, RMSProp and Adam) which are commonly used. In the experiments, we used the TensorFlow open source library produced by Google and e-commerce session data from RecSys Challenge 2015. The results using the optimal hyper-parameters found in this study are compared with those of RecSys Challenge 2015 participants.
#전자상거래 #추천시스템 #머신러닝 #순환신경망 #최적화 알고리즘 #텐서플로우 #e-commerce #recommendation system #machine learning #recurrent neural network #optimization algorithm #TensorFlow

Contents

요약
Abstract
1. 서론
2. 연구동향 및 관련기술
3. 성능평가 실험 환경
4. 실험결과 및 성능평가
5. 결론 및 향후 연구
References

References (16)

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J. Son, S. Kim, H. Kim, and S. Cho, Review and Analysis of Recommender Systems, Journal of KIIE, Vol. 41, No. 2, pp. 185-208, Apr. 2015. (in Korean) B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl, Analysis of Recommendation Algorithms for E-commerce, Proc. of 2nd ACM Conference on Electronic Commerce, pp. 158-167, 2000. B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, Session-based Recommendations with Recurrent Neural Networks, Proc. of the ICLR 2016, 2016. S. Wu, W. Ren, C. Yu, G. Chen, D. Zhang, and J. Zhu, Personal Recommendation Using Deep Recurrent Neural Network in NetEase, Proc. of the 32nd ICDE 2016, 2016. I. Sutskever, Training Recurrent Neural Netwroks, PhD thesis Uninversity Tronto, 2012.

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