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Refereed Papers
Track: Internet Monetization: Recommendation and Security
Paper Title:
Trust-Based Recommendation Systems: an Axiomatic Approach
Authors:
- Reid Andersen(Microsoft Research)
- Christian Borgs(Microsoft Research)
- Jennifer Chayes(Microsoft Research)
- Uriel Feige(Weizmann Institute of Science)
- Abraham Flaxman(Microsoft Research)
- Adam Kalai(Georgia Institute of Technology)
- Vahab Mirrokni(Microsoft Research)
- Moshe Tennenholtz(Technion-Israel Institute of Technology)
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.
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