document: Trust-based recommendation systems : an axiomatic approach High-quality , personalized recommendations are a key feature in many online systems . Since these systems often have explicit knowledge of social network structures , the recommendations may incorporate this information . This paper focuses on networks that represent trust and recommendation systems that incorporate these trust relationships . The goal of a trust-based recommendation system is to generate personalized recommendations by aggregating the opinions of other users in the trust network . In analogy to prior work on voting and ranking systems , we use the axiomatic approach from the theory of social choice . We develop a set of five natural axioms that a trust-based recommendation system might be expected to satisfy . Then , we show that no system can simultaneously satisfy all the axioms . However , for any subset of four of the five axioms we exhibit a recommendation system that satisfies those axioms . Next we consider various ways of weakening the axioms , one of which leads to a unique recommendation system based on random walks . We consider other recommendation systems , including systems based on personalized PageRank , majority of majorities , and minimum cuts , and search for alternative axiomatizations that uniquely characterize these systems . Finally , we determine which of these systems are incentive compatible , meaning that groups of agents interested in manipulating recommendations can not induce others to share their opinion by lying about their votes or modifying their trust links . This is an important property for systems deployed in a monetized environment .
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