Abstract
Trust-aware recommender systems(TaRSs) are based on the fact that most people tend to believe the advice given by their trusted sources. It solves most of the problems faced by traditional RSs. Collaborative filtering(CF) is the most popular technique used to filter the information to find the useful data for each user of the system but it faces some serious problems which include mainly data sparsity and cold users problem and moreover it is prone to malicious user attacks popularly known as Shilling attack Whereas TaRSs due to trust propagation does not require to know much about a new user but some explicit trust statements about other users from him and it can provide recommendations for the new user as good as an old user. In this paper, we are focusing on important aspects of TaRSs and the evolution of trust metrics which is most preferably used technique in TaRSs for calculating trust between two unknown users then finally summarized various trust metrics on basis of several measures such as coverage and precision.
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