# Safe Collaborative Filtering

arXiv: https://arxiv.org/abs/2306.05292 · HTML: https://mlsanity.com/paper/2306.05292/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 81 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 28 of 28)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Large-Scale Methods for Distributionally Robust Optimization](https://mlsanity.com/paper/2010.05893/) (2020)
- [Fast Matrix Factorization for Online Recommendation with Implicit Feedback](https://mlsanity.com/paper/1708.05024/) (2017)
- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [MLlib: Machine Learning in Apache Spark](https://mlsanity.com/paper/1505.06807/) (2015)
- [An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives](https://mlsanity.com/paper/2006.10138/) (2020)
- [Adaptive Sampling for Stochastic Risk-Averse Learning](https://mlsanity.com/paper/1910.12511/) (2019)
- [Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis](https://mlsanity.com/paper/2110.12459/) (2021)
- [An optimal randomized incremental gradient method](https://mlsanity.com/paper/1507.02000/) (2015)
- [Two-sided fairness in rankings via Lorenz dominance](https://mlsanity.com/paper/2110.15781/) (2021)
- [Smoothed Quantile Regression with Large-Scale Inference](https://mlsanity.com/paper/2012.05187/) (2020)
- [Sub-sampled Newton Methods with Non-uniform Sampling](https://mlsanity.com/paper/1607.00559/) (2016)
- [Revisiting the Performance of iALS on Item Recommendation Benchmarks](https://mlsanity.com/paper/2110.14037/) (2021)
- [Statistical Learning with Conditional Value at Risk](https://mlsanity.com/paper/2002.05826/) (2020)
- [A Generic Coordinate Descent Framework for Learning from Implicit Feedback](https://mlsanity.com/paper/1611.04666/) (2016)
- [Smoothing quantile regressions](https://mlsanity.com/paper/1905.08535/) (2019)
- [High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization](https://mlsanity.com/paper/2109.05640/) (2021)
- [PAC-Bayesian Bound for the Conditional Value at Risk](https://mlsanity.com/paper/2006.14763/) (2020)
- [Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons](https://mlsanity.com/paper/1507.04457/) (2015)
- [Efficient Training on Very Large Corpora via Gramian Estimation](https://mlsanity.com/paper/1807.07187/) (2018)
- [Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms](https://mlsanity.com/paper/1703.02624/) (2017)
- [Markov Random Fields for Collaborative Filtering](https://mlsanity.com/paper/1910.09645/) (2019)
- [Curse of "Low" Dimensionality in Recommender Systems](https://mlsanity.com/paper/2305.13597/) (2023)
- [Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs](https://mlsanity.com/paper/1603.03820/) (2016)
- [iALS++: Speeding up Matrix Factorization with Subspace Optimization](https://mlsanity.com/paper/2110.14044/) (2021)
- [A Unified Algorithm for Penalized Convolution Smoothed Quantile Regression](https://mlsanity.com/paper/2205.02432/) (2022)

## Built on by (most cited first, 1 of 1)

- [UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems](https://mlsanity.com/paper/2401.09034/) (2024)

## Abstract (arXiv)

Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance is also a vital determinant of success for personalized recommender systems to reduce the risk of losing users with low satisfaction. This study introduces a "safe" collaborative filtering method that prioritizes recommendation quality for less-satisfied users rather than focusing on the average performance. Our approach minimizes the conditional value at risk (CVaR), which represents the average risk over the tails of users' loss. To overcome computational challenges for web-scale recommender systems, we develop a robust yet practical algorithm that extends the most scalable method, implicit alternating least squares (iALS). Empirical evaluation on real-world datasets demonstrates the excellent tail performance of our approach while maintaining competitive computational efficiency.