Nearest Neighbor-based Collaborative Filtering Algorithm

A Nearest Neighbor-based Collaborative Filtering Algorithm is a collaborative filtering algorithm that is a NN-based algorithm.

• Context:
  • It can be implemented by a NN-based Recommendation System (to solve a NN-based recommendation task).
• Example(s):
  • ItemRank.
  • …
• Counter-Example(s):
  • a Deep Neural Network-based Recommendation Algorithm.
  • a Matrix Factorization-based Item Recommendation Algorithm.
• See: Collaborative Filtering System.

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References

2011

• (Desrosiers & Karypis, 2011) ⇒ Christian Desrosiers, and George Karypis. (2011). “A Comprehensive Survey of Neighborhood-based Recommendation Methods.” In: Recommender systems handbook. doi:10.1007/978-0-387-85820-3_4
  • QUOTE: 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, often observed in large commercial recommender systems, are discussed, and a few solutions to overcome these problems are presented.

2007

• (Gori & Pucci, 2007) ⇒ Marco Gori, and Augusto Pucci. (2007). “ItemRank: A Random-walk based Scoring Algorithm for Recommender Engines.” In: Proceedings of the 20th international joint conference on Artifical intelligence.

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