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

TL;DR

This survey comprehensively reviews neighborhood-based recommender systems, highlighting their simplicity, efficiency, and personalization capabilities, and covers traditional and advanced methods including matrix factorization and random walks.

Contribution

It provides a detailed overview of neighborhood-based recommendation methods, including design choices and practical implementation guidance, covering both traditional and modern approaches.

Findings

Traditional algorithms like k-nearest neighbors are still relevant.

Advanced methods such as matrix factorization enhance recommendation quality.

The survey offers practical insights for implementing neighborhood-based recommenders.

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.