Abstract:
Recommender systems are intended to help users make effective product and service choices, especially over the Internet. They are used in a variety of applications and have proven to be valuable for predicting the utility or relevance of a particular item and for providing personalized recommendations. State-of-the-art recommender systems focus on atomic (single) products or services and on individual users. This dissertation considers three ways of extending recommender systems: (1) to make composite (package) rather than atomic recommendations; (2) to use multiple rather than single criteria for recommendations; and, most importantly, (3) to support groups of diverse users or decision makers who might have different, even strongly conflicting, views on the weights of different criteria.
