Bimodal Cross-Validation Approach for Recommender Systems Diagnostics

Ch. 8. P. 185–195.

Ignatov D. I., Poelmans J.

Recommender systems are becoming an inseparable part of many modern Internet web sites and web shops. The quality of recommendations made may significantly influence the browsing experience of the user and revenues made by web site owners. Developers can choose between a variety of recommender algorithms; unfortunately no general scheme exists for evaluation of their recall and precision. In this chapter, the authors propose a method based on cross-validation for diagnosing the strengths and weaknesses of recommender algorithms. The method not only splits initial data into a training and test subsets, but also splits the attribute set into a hidden and visible part. Experiments were performed on a user-based and item-based recommender algorithm. These algorithms were applied to the MovieLens dataset, and the authors found classical user-based methods perform better in terms of recall and precision.

Keywords: машинное обучение, информационный поиск, information retrieval, рекомендательные системы, machine learning, Recommender Systems

In book

Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems

Naidenova X., Ignatov D. I. Hershey: IGI Global, 2012.