Hybrid Recommender System based on Autoencoders

24 Jun 2016Florian StrubRomaric GaudelJérémie Mary

A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Collaborative Filtering Douban U-CFN RMSE 0.7049 # 2
Collaborative Filtering Douban I-CFN RMSE 0.6911 # 1
Collaborative Filtering MovieLens 10M U-CFN RMSE 0.7954 # 13
Collaborative Filtering MovieLens 10M I-CFN RMSE 0.7767 # 10
Collaborative Filtering MovieLens 1M U-CFN RMSE 0.8574 # 7
Collaborative Filtering MovieLens 1M I-CFN RMSE 0.8321 # 5