Browse > Article
http://dx.doi.org/10.5351/KJAS.2021.34.3.329

Comparison of deep learning-based autoencoders for recommender systems  

Lee, Hyo Jin (Department of Statistics, Korea University)
Jung, Yoonsuh (Department of Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.3, 2021 , pp. 329-345 More about this Journal
Abstract
Recommender systems use data from customers to suggest personalized products. The recommender systems can be categorized into three cases; collaborative filtering, contents-based filtering, and hybrid recommender system that combines the first two filtering methods. In this work, we introduce and compare deep learning-based recommender system using autoencoder. Autoencoder is an unsupervised deep learning that can effective solve the problem of sparsity in the data matrix. Five versions of autoencoder-based deep learning models are compared via three real data sets. The first three methods are collaborative filtering and the others are hybrid methods. The data sets are composed of customers' ratings having integer values from one to five. The three data sets are sparse data matrix with many zeroes due to non-responses.
Keywords
autoencoder; deep learning; recommender system; sparse data matrix;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Wu Y, DuBois C, Zheng AX, and Ester M (2016). Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 153-162.
2 Zhang S, Yao L, Sun A, and Tay Y (2019). Deep learning based recommender system: A survey and new perspectives, ACM Computing Surveys (CSUR), 52, 1-38.   DOI
3 Zhang S, Yao L, and Xu X (2017). Autosvd++ an efficient hybrid collaborative filtering model via contractive auto-encoders. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 957-960.
4 Zheng N and Xue J (2009). Manifold Learning(pp.87-119), Springer, London.
5 Rifai S, Vincent P, Muller X, Glorot X, and Bengio Y (2011). Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th International Conference on Machine Learning June 2011, 833-840
6 Vincent P, Larochelle H, Bengio Y, and Manzagol PA (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, 1096-1103.
7 Aggarwal CC (2018). Neural Networks and Deep Learning, Springer, NewYork.
8 Harper FM and Konstan JA (2015). The movielens datasets: History and context, ACM Transactions on Interactive Intelligent Systems, 5, 1-19.   DOI
9 Koren Y (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 426-434.
10 Koren Y, Bell R, and Volinsky C (2009). Matrix factorization techniques for recommender systems, Computer, 42, 30-37.   DOI
11 Schafer JB, Frankowski D, Herlocker J, and Sen S (2007). Collaborative filtering recommender systems, The Adaptive Web(pp. 291-324), Springer, NewYork.
12 Kuchaiev O and Ginsburg B (2017). Training Deep Autoencoders for Collaborative Filtering, arXiv preprint arXiv:1708.01715.
13 Ni J, Li J, and McAuley J (2019). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 188-197.
14 Odaibo S (2019). Tutorial: Deriving the Standard Variational Autoencoder (vae) Loss Function, arXiv preprint arXiv:1907.08956.
15 Sedhain S, Menon AK, Sanner S, and Xie L (2015). Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on World Wide Web, 111-112.
16 Ali SM, Nayak GK, Lenka RK, and Barik RK (2018). Movie recommendation system using genome tags and content-based filtering, Advances in Data and Information Sciences(pp.85-94), Springer, NewYork.
17 Kingma DP and Welling M (2013). Auto-Encoding Variational Bayes, arXiv preprint arXIV:1312.6114.