Hybrid Recommender Systems

Recommender systems represent user preferences for the purpose of suggesting
items to purchase or examine. They have become fundamental applications in
electronic commerce and information access, providing suggestions that
effectively prune large information spaces so that users are directed toward those
items that best meet their needs and preferences. A variety of techniques have
been proposed for performing recommendation, including content-based,
collaborative, knowledge-based and other techniques. To improve performance,
these methods have sometimes been combined in hybrid recommenders. This
paper surveys the landscape of actual and possible hybrid recommenders, and
introduces a novel hybrid, EntreeC, a system that combines knowledge-based
recommendation and collaborative filtering to recommend restaurants. Further,
we show that semantic ratings obtained from the knowledge-based part of the
system enhance the effectiveness of collaborative filtering.


All of the known recommendation techniques have strengths and weaknesses, and manyresearchers have chosen to combine techniques in different ways. This article surveys the different recommendationtechniques being researched — analyzing them in terms of the data that supports the recommendations and thealgorithms that operate on that data — and examines the range of hybridization techniques that have been proposed.This analysis points to a number of possible hybrids that have yet to be explored. Finally, how adding ahybrid with collaborative filtering improved the performance of our knowledge-based recommender system Entree is discussed.

This links to the paper:

http://www-mmt.inf.tu-dresden.de/Lehre/Archiv/Sommersemester_04/Hauptseminar/papers/burke2002.pdf