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44
A Survey of Attack and Defense Techniques for Reputation Systems
"... Reputation systems provide mechanisms to produce a metric encapsulating reputation for a given domain for each identity within the system. These systems seek to generate an accurate assessment in the face of various factors including but not limited to unprecedented community size and potentially ad ..."
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Cited by 103 (3 self)
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Reputation systems provide mechanisms to produce a metric encapsulating reputation for a given domain for each identity within the system. These systems seek to generate an accurate assessment in the face of various factors including but not limited to unprecedented community size and potentially adversarial environments. We focus on attacks and defense mechanisms in reputation systems. We present an analysis framework that allows for general decomposition of existing reputation systems. We classify attacks against reputation systems by identifying which system components and design choices are the target of attacks. We survey defense mechanisms employed by existing reputation systems. Finally, we analyze several landmark systems in the peertopeer domain, characterizing their individual strengths and weaknesses. Our work contributes to understanding 1) which design components of reputation systems are most vulnerable, 2) what are the most appropriate defense mechanisms and 3) how these defense mechanisms can be integrated into existing or future reputation systems to make them resilient to attacks.
An Axiomatic Approach for Result Diversification
 WWW 2009 MADRID!
, 2009
"... Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diversify results to improve user satisfaction. In such a setting, the probability ranking principlebased approach of present ..."
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Cited by 101 (1 self)
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Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diversify results to improve user satisfaction. In such a setting, the probability ranking principlebased approach of presenting the most relevant results on top can be suboptimal, and hence the search engine would like to tradeoff relevance for diversity in the results. In analogy to prior work on ranking and clustering systems, we use the axiomatic approach to characterize and design diversification systems. We develop a set of natural axioms that a diversification system is expected to satisfy, and show that no diversification function can satisfy all the axioms simultaneously. We illustrate the use of the axiomatic framework by providing three example diversification objectives that satisfy different subsets of the axioms. We also uncover a rich link to the facility dispersion problem that results in algorithms for a number of diversification objectives. Finally, we propose an evaluation methodology to characterize the objectives and the underlying axioms. We conduct a large scale evaluation of our objectives based on two data sets: a data set derived from the Wikipedia disambiguation pages and a product database.
Improved bounds for computing kemeny rankings
 In AAAI’06, 620–626
, 2006
"... Voting (or rank aggregation) is a general method for aggregating the preferences of multiple agents. One voting rule of particular interest is the Kemeny rule, which minimizes the number of cases where the final ranking disagrees with a vote on the order of two alternatives. Unfortunately, Kemeny r ..."
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Cited by 54 (8 self)
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Voting (or rank aggregation) is a general method for aggregating the preferences of multiple agents. One voting rule of particular interest is the Kemeny rule, which minimizes the number of cases where the final ranking disagrees with a vote on the order of two alternatives. Unfortunately, Kemeny rankings are NPhard to compute. Recent work on computing Kemeny rankings has focused on producing good bounds to use in searchbased methods. In this paper, we extend on this work by providing various improved bounding techniques. Some of these are based on cycles in the pairwise majority graph, others are based on linear programs. We completely characterize the relative strength of all of these bounds and provide some experimental results.
Computing Slater rankings using similarities among candidates
 In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI
, 2006
"... Voting (or rank aggregation) is a general method for aggregating the preferences of multiple agents. One important voting rule is the Slater rule. It selects a ranking of the alternatives (or candidates) to minimize the number of pairs of candidates such that the ranking disagrees with the pairwi ..."
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Cited by 47 (7 self)
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Voting (or rank aggregation) is a general method for aggregating the preferences of multiple agents. One important voting rule is the Slater rule. It selects a ranking of the alternatives (or candidates) to minimize the number of pairs of candidates such that the ranking disagrees with the pairwise majority vote on these two candidates. The use of the Slater rule has been hindered by a lack of techniques to compute Slater rankings. In this paper, we show how we can decompose the Slater problem into smaller subproblems if there is a set of similar candidates. We show that this technique suffices to compute a Slater ranking in linear time if the pairwise majority graph is hierarchically structured. For the general case, we also give an efficient algorithm for finding a set of similar candidates. We provide experimental results that show that this technique significantly (sometimes drastically) speeds up search algorithms. Finally, we also use the technique of similar sets to show that computing an optimal Slater ranking is NPhard, even in the absence of pairwise ties.
Trustbased recommendation systems: An axiomatic approach
, 2008
"... Highquality, 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 which represent trust and recommendations which in ..."
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Cited by 42 (6 self)
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Highquality, 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 which represent trust and recommendations which incorporate trust relationships. The goal of a trustbased recommendation system is to generate personalized recommendations from known opinions and trust relationships. In analogy to prior work on voting and ranking systems, we use the axiomatic approach from the theory of social choice. We develop an natural set of five axioms which we desire any recommendation system exhibit. Then we show that no system can simultaneously satisfy all these axioms.
The Computational Complexity of Choice Sets
"... Social choice rules are often evaluated and compared by inquiring whether they satisfy certain desirable criteria such as the Condorcet criterion, which states that an alternative should always be chosen when more than half of the voters prefer it over any other alternative. Many of these criteria c ..."
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Cited by 26 (11 self)
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Social choice rules are often evaluated and compared by inquiring whether they satisfy certain desirable criteria such as the Condorcet criterion, which states that an alternative should always be chosen when more than half of the voters prefer it over any other alternative. Many of these criteria can be formulated in terms of choice sets that single out reasonable alternatives based on the preferences of the voters. In this paper, we consider choice sets whose definition merely relies on the pairwise majority relation. These sets include the Copeland set, the Smith set, the Schwartz set, von NeumannMorgenstern stable sets, the Banks set, and the Slater set. We investigate the relationships between these sets and completely characterize their computational complexity, which allows us to obtain hardness results for entire classes of social choice rules.
An axiomatic approach to personalized ranking systems
 In Proc. 20th International Joint Conference on Artificial Intelligence
, 2006
"... Personalized ranking systems and trust systems are an essential tool for collaboration in a multiagent environment. In these systems, trust relations between many agents are aggregated to produce a personalized trust rating of the agents. In this paper we introduce the first extensive axiomatic stu ..."
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Cited by 22 (6 self)
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Personalized ranking systems and trust systems are an essential tool for collaboration in a multiagent environment. In these systems, trust relations between many agents are aggregated to produce a personalized trust rating of the agents. In this paper we introduce the first extensive axiomatic study of this setting, and explore a wide array of wellknown and new personalized ranking systems. We adapt several axioms (basic criteria) from the literature on global ranking systems to the context of personalized ranking systems, and fully classify the set of systems that satisfy all of these axioms. We further show that all these axioms are necessary for this result. 1
Sum of Us: Strategyproof Selection from the Selectors
"... We consider directed graphs over a set of n agents, where an edge (i, j) is taken to mean that agent i supports or trusts agent j. Given such a graph and an integer k ≤ n, we wish to select a subset of k agents that maximizes the sum of indegrees, i.e., a subset of k most popular or most trusted age ..."
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Cited by 22 (7 self)
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We consider directed graphs over a set of n agents, where an edge (i, j) is taken to mean that agent i supports or trusts agent j. Given such a graph and an integer k ≤ n, we wish to select a subset of k agents that maximizes the sum of indegrees, i.e., a subset of k most popular or most trusted agents. At the same time we assume that each individual agent is only interested in being selected, and may misreport its outgoing edges to this end. This problem formulation captures realistic scenarios where agents choose among themselves, which can be found in the context of Internet search, social networks like Twitter, or reputation systems like Epinions. Our goal is to design mechanisms without payments that map each graph to a ksubset of agents to be selected and satisfy the following two constraints: strategyproofness, i.e., agents cannot benefit from misreporting their outgoing edges, and approximate optimality, i.e., the sum of indegrees of the selected subset of agents is always close to optimal. Our first main result is a surprising impossibility: for k ∈ {1,...,n − 1}, no deterministic strategyproof mechanism can provide a finite approximation ratio. Our second main result is a randomized strategyproof mechanism with an approximation ratio that
Computing the Banzhaf power index in network flow games
 In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007
, 2007
"... Preference aggregation is used in a variety of multiagent applications, and as a result, voting theory has become an important topic in multiagent system research. However, power indices (which reflect how much “real power ” a voter has in a weighted voting system) have received relatively little at ..."
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Cited by 18 (7 self)
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Preference aggregation is used in a variety of multiagent applications, and as a result, voting theory has become an important topic in multiagent system research. However, power indices (which reflect how much “real power ” a voter has in a weighted voting system) have received relatively little attention, although they have long been studied in political science and economics. The Banzhaf power index is one of the most popular; it is also welldefined for any simple coalitional game. In this paper, we examine the computational complexity of calculating the Banzhaf power index within a particular multiagent domain, a network flow game. Agents control the edges of a graph; a coalition wins if it can send a flow of a given size from a source vertex to a target vertex. The relative power of each edge/agent reflects its significance in enabling such a flow, and in realworld networks could be used, for example, to allocate resources for maintaining parts of the network. We show that calculating the Banzhaf power index of each agent in this network flow domain is #Pcomplete. We also show that for some restricted network flow domains there exists a polynomial algorithm to calculate agents ’ Banzhaf power indices.