# AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders

arXiv: https://arxiv.org/abs/1704.00551 · HTML: https://mlsanity.com/paper/1704.00551/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2017.

## Built on by (most cited first, 20 of 20)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
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- [CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention](https://mlsanity.com/paper/2204.06519/) (2022)
- [Hyperbolic Diffusion Recommender Model](https://mlsanity.com/paper/2504.01541/) (2025)
- [GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation](https://mlsanity.com/paper/2111.11293/) (2021)
- [Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks](https://mlsanity.com/paper/2306.00342/) (2023)
- [SVD-AE: Simple Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/2405.04746/) (2024)
- [MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings](https://mlsanity.com/paper/2106.04209/) (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)
- [Adaptive Divergence-based Non-negative Latent Factor Analysis](https://mlsanity.com/paper/2203.16214/) (2022)
- [UserReg: A Simple but Strong Model for Rating Prediction](https://mlsanity.com/paper/2102.07601/) (2021)
- [SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System](https://mlsanity.com/paper/2110.00452/) (2021)
- [Reciprocity in Machine Learning](https://mlsanity.com/paper/2202.09480/) (2022)
- [PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis](https://mlsanity.com/paper/2205.02591/) (2022)
- [FusionDeepMF: A Dual Embedding based Deep Fusion Model for Recommendation](https://mlsanity.com/paper/2210.05338/) (2022)
- [Meta-Shop: Improving Item Advertisement For Small Businesses](https://mlsanity.com/paper/2212.01414/) (2022)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2408.02704/) (2024)
- [A Novel Spatiotemporal Coupling Graph Convolutional Network](https://mlsanity.com/paper/2408.07087/) (2024)

## Abstract (arXiv)

Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which are usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly model content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.