Abstract

Title: Hybrid Recommender Systems: A Comparative Study
Published: September 2007
Authors: Robin Burke
Abstract: Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 53 hybrids including some novel combinations are examined and experimentally evaluated. The study finds that cascade and augmented hybrids work well, especially when the two components have differing strengths.
Keywords: recommender systems, evaluation, comparative study
Full Paper: [doc]