Meta-recommendation systems: User-controlled integration of diverse recommendations

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They do so by connecting users with information regarding the content of recommended items or the opinions of other individuals. Such systems have become powerful tools in domains such as electronic commerce, digital libraries, and knowledge management. In this paper, we address such systems and introduce a new class of recommender system called meta-recommenders. Meta-recommenders provide users with personalized control over the generation of a single recommendation list formed from a combination of rich data using multiple information sources and recommendation techniques. We discuss experiments conducted to aid in the design of interfaces for a meta-recommender in the domain of movies. We demonstrate that meta-recommendations fill a gap in the current design of recommender systems. Finally, we consider the challenges of building real-world, usable meta-recommenders across a variety of domains.