mlsanityData to 7 Oct 2026

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

arXiv 2109.04584 ↗First posted 9 Sep 2021cs.IR cs.LGcited by 8

Not on the map. Why?

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 90 references that are in the map. First posted on arXiv 9 Sep 2021.

Builds on

5 of 90 references.

References · 5

  1. 2019Embarrassingly Shallow Autoencoders for Sparse Datacited by 93
  2. 2013Empirical Analysis of Predictive Algorithms for Collaborative Filteringcited by 47
  3. 2015EigenRec: Generalizing PureSVD for Effective and Efficient Top-N Recommendationscited by 5
  4. 2019Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walkscited by 2
  5. 2015Top-N recommendations in the presence of sparsity: An NCD-based approachcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 0; 2025: 2; 2026: 3.

8 papers.

  1. 2023Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluationcited by 2
  2. 2023Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenderscited by 1
  3. 2025Adaptively-weighted Nearest Neighbors for Matrix Completioncited by 1
  4. 2025Aligning Language Models with Investor and Market Behavior for Financial Recommendationscited by 1
  5. 2023Recommender Systems: A Primercited by 0
  6. 2026Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Losscited by 0
  7. 2026Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measurecited by 0
  8. 2026GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphscited by 0