Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems
Abstract
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.
Keywords
Cite
@article{arxiv.2109.04584,
title = {Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems},
author = {Athanasios N. Nikolakopoulos and Xia Ning and Christian Desrosiers and George Karypis},
journal= {arXiv preprint arXiv:2109.04584},
year = {2021}
}
Comments
50 pages; Chapter in the Recommender Systems Handbook, 3rd Edition (to appear)