Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems [preprint]
September 9, 2021
Athanasios N Nikolakopoulos, Xia Ning, Christian Desrosiers, George Karypis (professor)
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.
Link to full paper