# Hybrid Recommender System based on Autoencoders

arXiv: https://arxiv.org/abs/1606.07659 · HTML: https://mlsanity.com/paper/1606.07659/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

## Built on by (most cited first, 14 of 14)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network](https://mlsanity.com/paper/2110.04002/) (2021)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures](https://mlsanity.com/paper/2404.14942/) (2024)
- [GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation](https://mlsanity.com/paper/2111.11293/) (2021)
- [CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation](https://mlsanity.com/paper/2108.10511/) (2021)
- [Multi-target prediction for dummies using two-branch neural networks](https://mlsanity.com/paper/2104.09967/) (2021)
- [Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems](https://mlsanity.com/paper/2201.03158/) (2022)
- [Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering](https://mlsanity.com/paper/2203.07463/) (2022)
- [A Scalable Recommendation Engine for New Users and Items](https://mlsanity.com/paper/2209.06128/) (2022)
- [One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations](https://mlsanity.com/paper/2208.14594/) (2022)
- [SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation](https://mlsanity.com/paper/2209.04154/) (2022)
- [Ae2I: A Double Autoencoder for Imputation of Missing Values](https://mlsanity.com/paper/2301.06633/) (2023)
- [Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization](https://mlsanity.com/paper/2306.13050/) (2023)

## Abstract (arXiv)

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.