The problem of collaborative filtering is to predict how well a user will like an item that he or she has not rated, given a set of historical ratings for this and other items from a community of users. A plethora of collaborative filtering algorithms have been proposed in related literature. One of the most prevalent families of collaborative filtering algorithms are neighborhood-based ones, which calculate a prediction of how much a user will like a particular item, based on how other users with similar preferences have rated this item. This chapter aims to provide an overview of various proposed design options for neighborhood-based collaborative filtering systems, in order to facilitate their better understanding, as well as their study and implementation by recommender systems’ researchers and developers. For this purpose, the chapter extends a series of design stages of neighborhood-based algorithms, as they have been initially identified by related literature on collaborative filtering systems. Then, it reviews proposed alternatives for each design stage and provides an overview of potential design options.