The Internet provides large varieties of content, which renders consumption difficult for users. However, recommender systems filter and personalize content according to individual preferences and deliver solutions that take the problem of information overload into account. Previous studies show different approaches to classify existing recommender technologies. Nevertheless, these do not yet integrate social networking information. This study offers a systematic and up-to-date overview of three generations of recommender system technologies, including the latest development of social recommender systems. Also, the study delivers a typology and classification framework with the components of all types of recommender systems and their interactions. Separated between input, process (performed by technology and parameters) and output, we provide an overview to understand and visualize the recommendation process. Our results provide comprehensive insights in current recommender system technologies and are helpful for the design of business models and digitalization strategies.