A comprehensive survey of neighborhood-based recommendation methods

Xia Ning, Christian Desrosiers, and George Karypis
In Recommender Systems Handbook; 2nd edition, 2015
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Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses the essential decisions that are required while implementing a neighborhood-based recommender system, and gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, of- ten observed in large commercial recommender systems, are discussed, and some solutions to overcome these problems are presented.
Research topics: Collaborative filtering