mlsanityData to 7 Oct 2026

Hybrid Recommender System based on Autoencoders

arXiv 1606.07659 ↗First posted 24 Jun 2016cs.LG cs.IRcited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2016.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 3; 2024: 1.

14 papers.

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  1. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  2. 2021Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Networkcited by 28
  3. 2023Recent Developments in Recommender Systems: A Surveycited by 14
  4. 2024Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasurescited by 14
  5. 2021GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendationcited by 7
  6. 2021CMML: Contextual Modulation Meta Learning for Cold-Start Recommendationcited by 4
  7. 2021Multi-target prediction for dummies using two-branch neural networkscited by 2
  8. 2022Collaborative Reflection-Augmented Autoencoder Network for Recommender Systemscited by 2
  9. 2022Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filteringcited by 1
  10. 2022A Scalable Recommendation Engine for New Users and Itemscited by 1
  11. 2022One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representationscited by 0
  12. 2022SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendationcited by 0
2 more
  1. 2023Ae2I: A Double Autoencoder for Imputation of Missing Valuescited by 0
  2. 2023Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorizationcited by 0