This chapter by Robin Burke from DePaul University explores the development and performance of hybrid web recommender systems. It surveys two-part hybrid systems that combine four different recommendation techniques—collaborative, content-based, demographic, and knowledge-based—and seven hybridization strategies. The study examines 41 implementations of these hybrids, including novel combinations, and finds that cascade and augmented hybrids perform well, especially when combining components with complementary strengths. The chapter highlights the advantages of hybrid systems in addressing known shortcomings of individual techniques, such as the cold-start problem for collaborative and content-based systems. It also discusses the knowledge sources used in these systems, including user preferences, domain knowledge, and demographic profiles, and provides a quantitative comparison of the effectiveness of different hybridization methods.This chapter by Robin Burke from DePaul University explores the development and performance of hybrid web recommender systems. It surveys two-part hybrid systems that combine four different recommendation techniques—collaborative, content-based, demographic, and knowledge-based—and seven hybridization strategies. The study examines 41 implementations of these hybrids, including novel combinations, and finds that cascade and augmented hybrids perform well, especially when combining components with complementary strengths. The chapter highlights the advantages of hybrid systems in addressing known shortcomings of individual techniques, such as the cold-start problem for collaborative and content-based systems. It also discusses the knowledge sources used in these systems, including user preferences, domain knowledge, and demographic profiles, and provides a quantitative comparison of the effectiveness of different hybridization methods.