Autoencoder has been widely adopted into Collaborative Filtering (CF) for recommendation system. A classic CF problem is inferring the missing rating in an MxN matrix R where R(i, j) is the ratings given by the ith user to the jth item. This project is a Keras implementation of AutoRec [1] and Deep AutoRec [2] with additional experiments such as the impact of default rating of users or ratings.
The Dataset I used for this project is MovieLens 1M Dataset and can be downloaded from here.
The preprocessing of the dataset can be found in this Jupyter Notebook
The implementation of models in Keras can be found in this Jupyter Notebook
[1] Sedhain, Suvash, et al. "Autorec: Autoencoders meet collaborative filtering." Proceedings of the 24th International Conference on World Wide Web. ACM, 2015
[2] Kuchaiev, Oleksii, and Boris Ginsburg. "Training deep autoencoders for collaborative filtering." arXiv preprint arXiv:1708.01715 (2017).
[3]Wu, Yao, et al. "Collaborative denoising auto-encoders for top-n recommender systems." Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 2016.
[4]Strub, Florian, Jérémie Mary, and Romaric Gaudel. "Hybrid collaborative filtering with autoencoders." arXiv preprint arXiv:1603.00806 (2016).
https://github.com/NVIDIA/DeepRecommender
https://github.com/gtshs2/Autorec