Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems
This is an incremental work that synthesizes existing knowledge for researchers and practitioners in recommender systems.
The paper provides a comprehensive survey of neighborhood-based methods for recommender systems, covering traditional algorithms like k-nearest neighbors and advanced approaches such as matrix factorization, sparse coding, and random walks, without presenting new experimental results or numbers.
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.