In this time of information overload, most people use many different approaches to make decisions about what to purchase, the way to spend their leisure time, and also whom thus far. Recommender systems automate a few of those strategies with the objective of offering affordable, private, and high quality recommendations. This publication provides a summary of approaches to creating advanced recommender systems. The authors present current algorithmic strategies for creating personalized purchasing suggestions, for example collaborative and content-based filtering, in addition to more interactive and knowledge-based methods. They also talk about how to assess the efficacy of recommender systems and illustrate the processes with practical case studies. The last chapters cover emerging issues like recommender systems in the societal net and customer purchasing behaviour concept.