A Comparative Study of Collaborative Filtering Algorithms
This work addresses the problem of algorithm selection for researchers and practitioners in collaborative filtering, but it is incremental as it synthesizes existing methods rather than introducing new ones.
The paper conducted a comparative study of collaborative filtering algorithms to determine which techniques perform best under various conditions, reporting conclusions based on factors like dataset size, sparsity, and performance criteria.
Collaborative filtering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing several collaborative filtering techniques -- both classic and recent state-of-the-art -- in a variety of experimental contexts. Specifically, we report conclusions controlling for number of items, number of users, sparsity level, performance criteria, and computational complexity. Our conclusions identify what algorithms work well and in what conditions, and contribute to both industrial deployment collaborative filtering algorithms and to the research community.