# Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems

arXiv: https://arxiv.org/abs/2109.04584 · HTML: https://mlsanity.com/paper/2109.04584/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 90 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [Empirical Analysis of Predictive Algorithms for Collaborative Filtering](https://mlsanity.com/paper/1301.7363/) (2013)
- [EigenRec: Generalizing PureSVD for Effective and Efficient Top-N Recommendations](https://mlsanity.com/paper/1511.06033/) (2015)
- [Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks](https://mlsanity.com/paper/1909.03579/) (2019)
- [Top-N recommendations in the presence of sparsity: An NCD-based approach](https://mlsanity.com/paper/1507.00043/) (2015)

## Built on by (most cited first, 8 of 8)

- [Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation](https://mlsanity.com/paper/2305.09330/) (2023)
- [Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders](https://mlsanity.com/paper/2308.06885/) (2023)
- [Adaptively-weighted Nearest Neighbors for Matrix Completion](https://mlsanity.com/paper/2505.09612/) (2025)
- [Aligning Language Models with Investor and Market Behavior for Financial Recommendations](https://mlsanity.com/paper/2510.15993/) (2025)
- [Recommender Systems: A Primer](https://mlsanity.com/paper/2302.02579/) (2023)
- [Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss](https://mlsanity.com/paper/2603.07402/) (2026)
- [Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure](https://mlsanity.com/paper/2604.26983/) (2026)
- [GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs](https://mlsanity.com/paper/2605.08074/) (2026)

## Abstract (arXiv)

Collaborative recommendation approaches based on nearest-neighbors are still highly popular today due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter offers a comprehensive survey of neighborhood-based methods for the item recommendation problem. It presents the main characteristics and benefits of such methods, describes key design choices for implementing a neighborhood-based recommender system, and gives practical information on how to make these choices. A broad range of methods is covered in the chapter, including traditional algorithms like k-nearest neighbors as well as advanced approaches based on matrix factorization, sparse coding and random walks.