@InProceedings{andersen08a,
  author =	 { Reid Andersen and Christian Borgs and Jennifer
                  Chayes and Uriel Feige and Abraham Flaxman and Adam
                  Kalai and Vahab Mirrokni and Moshe Tennenholtz },
  title =	 {Trust-based recommendation systems: an axiomatic
                  approach},
  booktitle =	 {Proceeding of the 17th international conference on
                  World Wide Web},
  year =	 2008,
  isbn =	 {978-1-60558-085-2},
  pages =	 {199--208},
  location =	 {Beijing, China},
  doi =		 {10.1145/1367497.1367525},
  publisher =	 {{ACM}},
  address =	 {New York, NY, USA},
  abstract =	 {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. },
  cluster = 	 {9171789468632490605},
  url = 	 {http://jmvidal.cse.sc.edu/library/andersen08a.pdf},
}