텐서 데이터를 위한 딥 오토인코더 이용 추천 시스템
Recommendation system using Deep Autoencoder for Tensor data
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초록
These days, as interest in the recommendation system with deep learning is increasing, a number of related studies to develop a performance for collaborative filtering through autoencoder, a state-of-the-art deep learning neural network architecture has advanced considerably. The purpose of this study is to propose autoencoder which is used by the recommendation system to predict ratings, and we added more hidden layers to the original architecture of autoencoder so that we implemented deep autoencoder with 3 to 5 hidden layers for much deeper architecture. In this paper, therefore we make a comparison between the performance of them. In this research, we use 2-dimensional arrays and 3-dimensional tensor as the input dataset. As a result, we found a correlation between matrix entry of the 3-dimensional dataset such as item-time and user-time and also figured out that deep autoencoder with extra hidden layers generalized even better performance than autoencoder.
Abstract
These days, as interest in the recommendation system with deep learning is increasing, a number of related studies to develop a performance for collaborative filtering through autoencoder, a state-of-the-art deep learning neural network architecture has advanced considerably. The purpose of this study is to propose autoencoder which is used by the recommendation system to predict ratings, and we added more hidden layers to the original architecture of autoencoder so that we implemented deep autoencoder with 3 to 5 hidden layers for much deeper architecture. In this paper, therefore we make a comparison between the performance of them. In this research, we use 2-dimensional arrays and 3-dimensional tensor as the input dataset. As a result, we found a correlation between matrix entry of the 3-dimensional dataset such as item-time and user-time and also figured out that deep autoencoder with extra hidden layers generalized even better performance than autoencoder.
참고문헌 (12)
- J. B. Schafer, "The adaptive web", pp.291~324 (2007), Springer
- G. Adomavicius, "Recommender systems handbook", pp.217~253 (2011), Springer
- Y. Koren, "Matrix factorization techniques for recommender systems", Computer, 30(8), pp.30~37 (2009)
- Y. LeCun, "Deep learning", nature, 521(7553), pp.436~ (2015)
- R. Salakhutdinov, "Restricted Boltzmann machines for collaborative filtering", Proceedings of the 24th international conference on Machine learning, pp.791~798 (2007), ACM
- J. Bennett, "The netflix prize", Proceedings of KDD cup and workshop, pp.35~ (2007)
- C. A. Gomez-Uribe, "The netflix recommender system : Algorithms, business value, and innovation", ACM Transactions on Management Information Systems(TMIS), 6(4), pp.13~ (2016)
- P. Covington, "Deep neural networks for youtube recommendations", Proceedings of the 10th ACM Conference on Recommender Systems, pp.191~198 (2016), ACM
- N. Jones, "Computer science: The learning machines", Nature News, 505(7482), pp.146~ (2014)
- "MovieLens"[URL]
- "BigTensor"[URL]
- "Yelp"[URL]