Collective attention and ranking methods

In a world with a tremendous amount of choices, ranking systems are becoming increasingly important in helping individuals to find information relevant to them. As such, rankings play a crucial role of influencing the attention that is devoted to the various alternatives. This role generates a feedback when the ranking is based on citations, as is the case for PageRank used by Google. The attention bias due to published rankings affects new stated opinions (citations), which will, in turn, affect the next ranking. The purpose of this paper is to investigate this feedback by studying some simple but reasonable dynamics. We show that the long run behavior of the process much depends on the preferences, in particular on their diversity, and on the used ranking method. Two main families of methods are investigated, one based on the notion of 'handicaps', the other one on the notion of peers' rankings

Collective attention and ranking methods

In a world with a tremendous amount of choices, ranking systems are becoming increasingly important in helping individuals to find information relevant to them. As such, rankings play a crucial role of influencing the attention that is devoted to the various alternatives. This role generates a feedback when the ranking is based on citations, as is the case for PageRank used by Google. The attention bias due to published rankings affects new stated opinions (citations), which will, in turn, affect the next ranking. The purpose of this paper is to investigate this feedback by studying some simple but reasonable dynamics. We show that the long run behavior of the process much depends on the preferences, in particular on their diversity, and on the used ranking method. Two main families of methods are investigated, one based on the notion of 'handicaps', the other one on the notion of peers' rankings.Ranking ; Scoring ; Invariant method ; Peers' method ; Attention ; Handicap ; Scaling matrix ; Dynamics through influence

Trust-based recommendation systems: an axiomatic approach

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 which represent trust and recommendations which incorporate trust relationships. The goal of a trust-based 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. We also exhibit systems which satisfy any four of the five axioms. Next we consider ways of weakening the axioms, which can lead to a unique recommendation system based on random walks. We consider other recommendation systems (personal page rank, majority of majorities, and min cut) and search for alternative axiomatizations which uniquely characterize these systems. Finally, we determine which of these systems are incentive compatible. This is an important property for systems deployed in a monetized environment: groups of agents interested in manipulating recommendations to make others share their opinion have nothing to gain from lying about their votes or their trust links

Trust management schemes for peer-to-peer networks

Peer-to-peer (P2P) networking enables users with similar interests to exchange, or obtain files. This network model has been proven popular to exchange music, pictures, or software applications. These files are saved, and most likely executed, at the downloading host. At the expense of this mechanism, worms, viruses, and malware find an open front door to the downloading host and gives them a convenient environment for successful proliferation throughout the network. Although virus detection software is currently available, this countermeasure works in a reactive fashion, and in most times, in an isolated manner. A trust management scheme is considered to contain the proliferation of viruses in P2P networks. Specifically, a cooperative and distributed trust management scheme based on a two-layer approach to bound the proliferation of viruses is proposed. The new scheme is called double-layer dynamic trust (DDT) management scheme. The results show that the proposed scheme bounds the proliferation of malware. With the proposed scheme, the number of infected hosts and the proliferation rate are limited to small values. In addition, it is shown that network activity is not discouraged by using the proposed scheme. Moreover, to improve the efficiency on the calculation of trust values of ratio based normalization models, a model is proposed for trust value calculation using a three-dimensional normalization to represent peer activity with more accuracy than that of a conventional ratio based normalization. Distributed network security is also considered, especially in P2P network security. For many P2P systems, including ad hoc networks and online markets, reputation systems have been considered as a solution for mitigating the affects of malicious peers. However, a sybil attack, wherein forging identities is performed to unfairly and arbitrarily influence the reputation of peers in a network or community. To defend against sybil attack, each reported transaction, which is used to calculate trust values, is verified. In this thesis, it is shown that peer reputation alone cannot bound network subversion of a sybil attack. Therefore, a new trust management framework, called Sybildefense, is introduced. This framework combines a trust management scheme with a cryptography mechanism to verify different transaction claims issue by peers, including those bogus claims of sybil peers. To improve the efficiency on the identification of honest peers from sybil peers, a k-means clustering mechanism is adopted. Moreover, to include a list of peer’s trustees in a warning messages is proposed to generate a local table for a peer that it is used to identify possible clusters of sybil peers. The defensive performance of these algorithms are compared under sybil attacks. The performance results show that the proposed framework (Sybildefense) can thwart sybil attacks efficiently