# Reenvisioning Collaborative Filtering vs Matrix Factorization

arXiv: https://arxiv.org/abs/2107.13472 · HTML: https://mlsanity.com/paper/2107.13472/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [Managing Popularity Bias in Recommender Systems with Personalized Re-ranking](https://mlsanity.com/paper/1901.07555/) (2019)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [Addressing the Item Cold-start Problem by Attribute-driven Active Learning](https://mlsanity.com/paper/1805.09023/) (2018)
- [Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation](https://mlsanity.com/paper/2103.02590/) (2021)
- [How to make latent factors interpretable by feeding Factorization machines with knowledge graphs](https://mlsanity.com/paper/1909.05038/) (2019)
- [Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems](https://mlsanity.com/paper/2007.11893/) (2020)

## Built on by (most cited first, 6 of 6)

- [CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation](https://mlsanity.com/paper/2403.06447/) (2024)
- [When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?](https://mlsanity.com/paper/2305.01801/) (2023)
- [A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation](https://mlsanity.com/paper/2201.08150/) (2022)
- [Job recommendations: benchmarking of collaborative filtering methods for classifieds](https://mlsanity.com/paper/2301.07946/) (2023)
- [Matrix Factorization in Tropical and Mixed Tropical-Linear Algebras](https://mlsanity.com/paper/2309.13914/) (2023)
- [Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passing](https://mlsanity.com/paper/2503.07823/) (2025)

## Abstract (arXiv)

Collaborative filtering models based on matrix factorization and learned similarities using Artificial Neural Networks (ANNs) have gained significant attention in recent years. This is, in part, because ANNs have demonstrated good results in a wide variety of recommendation tasks. The introduction of ANNs within the recommendation ecosystem has been recently questioned, raising several comparisons in terms of efficiency and effectiveness. One aspect most of these comparisons have in common is their focus on accuracy, neglecting other evaluation dimensions important for the recommendation, such as novelty, diversity, or accounting for biases. We replicate experiments from three papers that compare Neural Collaborative Filtering (NCF) and Matrix Factorization (MF), to extend the analysis to other evaluation dimensions. Our contribution shows that the experiments are entirely reproducible, and we extend the study including other accuracy metrics and two statistical hypothesis tests. We investigated the Diversity and Novelty of the recommendations, showing that MF provides a better accuracy also on the long tail, although NCF provides a better item coverage and more diversified recommendations. We discuss the bias effect generated by the tested methods. They show a relatively small bias, but other recommendation baselines, with competitive accuracy performance, consistently show to be less affected by this issue. This is the first work, to the best of our knowledge, where several evaluation dimensions have been explored for an array of SOTA algorithms covering recent adaptations of ANNs and MF. Hence, we show the potential these techniques may have on beyond-accuracy evaluation while analyzing the effect on reproducibility these complementary dimensions may spark. Available at github.com/sisinflab/Reenvisioning-the-comparison-between-Neural-Collaborative-Filtering-and-Matrix-Factorization