Brodsky, AlexanderMengash, Hanan Abdullah2016-09-282016-09-282016https://hdl.handle.net/1920/10475Recommender 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.164 pagesenCopyright 2016 Hanan Abdullah MengashComputer scienceInformation technologyArtificial intelligenceDecision guidanceGroup decision-makingGroup recommender systemMulti-criteria optimizationPackage recommendationsRenewable energy sources investmentDissertation