Hybrid Recommender System based on Autoencoders

Abstract

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. In current paper, we enhanced that architecture (i) by using a loss function adapted to input data with missing values, and (ii) by incorporating side information. The experiments demonstrate that while side information only slightly improve the test error averaged on all users/items, it has more impact on cold users/items.

arXiv commentarXiv admin note: substantial text overlap with arXiv:1603.00806

Cited by in corpus (8)

• Deep Learning based Recommender System: A Survey and New Perspectives
• A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
• Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
• Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems
• Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios
• Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems
• Collaborative Filtering with Label Consistent Restricted Boltzmann Machine
• Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization