Recommender systems suggest the most appropriate items to users in
order to help customers to find the most relevant items and facilitate sales.
Collaborative filtering recommendation algorithm is the most successful
technique for recommendation. In view of the fact that collaborative filtering
systems depend on neighbors as the source of information, the recommendation
quality of this approach depends on the neighbor’s selection. However,
selecting neighbors can either stem from similarity or trust metrics. In this
paper, we analyze these two types of neighbor’s selection metrics used in the
field of recommendation in the literature. For each type, we first define it and
then review different proposed metrics.