This thesis proposes a novel recommendation approach to take advantage of the information available in user-created lists. Our approach assumes associations among any two items appearing in a list together. We consider two different ways to calculate the strength of item-item associations: frequency of co-occurrence, and sum of Bayesian ratings (SBR) of all lists containing the item pair. The latter takes into consideration not only the number of lists the items have co-appeared in, but also the quality of the lists. We collected a data set of user ratings for books along with Listmania lists on Amazon.com using Amazon Web Services (AWS). Our method shows superior performance to existing user-based and item-based collaborative filtering approaches according to the resulted Mean Absolute Error (MAE), coverage, precision and recall.