Dimensionality reduction

Statistics

Principal component analysis

Data mining

Computer science

Collaborative filtering

Regression

Collinearity

Mathematics

Artificial intelligence

Cluster analysis

Recommender system

Machine learning

Decision Support Systems

Traditional Collaborative Filtering (CF) recommender systems recommend the items to users based on their single ratings which are used to match similar users. In multi-criteria CF recommender systems, however, multi-criteria ratings are used instead of single ratings which can significantly improve the accuracy of traditional CF algorithms. This research proposes a new recommendation method using Classification and Regression Tree (CART) and Expectation Maximization (EM) for accuracy improvement of multi-criteria recommender systems. We also apply Principal Component Analysis (PCA) for dimensionality reduction and to address multi-collinearity induced from the interdependencies among criteria in multi-criteria CF datasets. Experimental results on Yahoo! Movies and TripAdvisor datasets demonstrated that the proposed method significantly improve recommendation accuracy in case of precision.

A Multi-Criteria Collaborative Filtering Recommender System Using Clustering and Regression Techniques