A Reputation Management Scheme Based on Global Trust Model for Peer-to-Peer Virtual Communities.
ABSTRACT Peer-to-peer virtual communities are often established dynamically with peers that are unrelated and unknown to each other.
Peers have to manage the risk involved with the transactions without prior knowledge about each other’s reputation. SimiTrust, a reputation management scheme, is proposed for P2P virtual communities. A unique global trust value, computed by aggregating
similarity-weighted recommendations of the peers who have interacted with him and reflecting the degree that the community
as a whole trusts a peer, is assigned to each peer in the community. Different from previous global-trust based schemes, SimiTrust
does not need any pre-trusted peers to ensure algorithm convergence and invalidates the assumption that the peers with high
trust value will give the honest recommendation. Theoretical analyses and experiments show that the scheme is still robust
under more general conditions where malicious peers cooperate in an attempt to deliberately subvert the system, converges
more quickly and decreases the number of inauthentic files downloaded more effectively than previous schemes.
- SourceAvailable from: Mario Schlosser[show abstract] [hide abstract]
ABSTRACT: Assessing the performance of peer-to-peer algorithms such as topology construction protocols, reputation algorithms or search algorithms is impossible without simulations since testing new algorithms by deploying them in an existing P2P network is prohibitively expensive. However, some P2P algorithms are sensitive to the network and traffic models that are used in the simulations. In order to produce realistic results, we therefore require simulations that resemble real-world P2P networks as closely as possible. In this paper, we describe the Query-Cycle Simulator, a simulator for P2P file-sharing networks. We link the Query-Cycle Simulator to measurements on existing P2P networks and discuss open issues in simulating these networks. We believe it is of major importance to understand interactions and activities in a variety of P2P networks, this work should serve as a first attempt to do so.06/2003;
Conference Proceeding: Item-based collaborative filtering recommendation algorithmus[show abstract] [hide abstract]
ABSTRACT: Recommender systems apply knowledge discovery techniques to the problem of making personalized recom- mendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative filtering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative filtering techniques. Item- based techniques first analyze the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different item-based recommendation generation algorithms. We look into different techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vec- tors) and different techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available user-based algorithms.01/2001
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ABSTRACT: We consider the free-rider problem in peer-to-peer file sharing networks such as Napster: that individual users are provided with no incentive for adding value to the network. We examine the design implications of the assumption that users will selfishly act to maximize their own rewards, by constructing a formal game theoretic model of the system and analyzing equilibria of user strategies under several novel payment mechanisms. We support and extend this work with results from experiments with a multi-agent reinforcement learning model.10/2001;