By Xin Liu, Anwitaman Datta, Ee-Peng Lim
Computational belief versions and computer Learning presents a close advent to the idea that of belief and its software in numerous machine technological know-how components, together with multi-agent structures, on-line social networks, and conversation platforms. selecting belief modeling demanding situations that can't be addressed by means of conventional methods, this book:
- Explains how reputation-based platforms are used to figure out belief in various on-line communities
- Describes how computing device studying innovations are hired to construct strong attractiveness systems
- Explores unique ways to selecting credibility of resources—one the place the human function is implicit, and person who leverages human enter explicitly
- Shows how choice aid will be facilitated by way of computational belief models
- Discusses collaborative filtering-based belief conscious suggestion systems
- Defines a framework for translating a belief modeling challenge right into a studying problem
- Investigates the objectivity of human suggestions, emphasizing the necessity to filter outlying opinions
Computational belief versions and computer studying effectively demonstrates how novel desktop studying concepts can increase the accuracy of belief assessment.
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Also, it assumes that there are some peers in the market who are already known to be trustworthy. Xiong et al. [288] proposed a more efficient solution called PeerTrust to effectively evaluate the trustworthiness of peers and identify various malicious behaviors. , a transaction context factor to differentiate between transactions and a community context factor to address community specific issues) in computing the trustworthiness of peers. 6: PeerTrust system architecture. performed by peer u with all other peers, S(u, i) is the normalized satisfaction u receives in the ith transaction, p(u, i) is the participating peer in the ith transaction, Cr(p(u, i)) is the credibility of peer p(u, i), T F (u, i) is the adaptive transaction context factor and CF (u) is the adaptive community context factor.
Reputation systems in e-commerce play a vital role in distinguishing honest behavior of buyers and sellers from dishonest ones. , buyers providing feedbacks) provide misleading opinions about sellers, to alter their trust scores. com concerning malicious reviews due to which many small businesses from hospitality industry suffered. 4 Many trust schemes for multi-agent e-marketplaces have been proposed to deal with the unfair rating problem. The Beta Reputation System (BRS) [115] calculates seller reputation using a probabilistic model based on the beta probability density function, which can be used to represent probability distributions of binary events.
All peers will converge to the same values representing global reputation). EigenTrust uses distributed hash table (DHT) to store and lookup global reputation scores. A score manager is responsible for calculating, storing and Introduction 11 communicating reputation scores for the identities who fall in the score manager’s responsibility range in the DHT. The use of multiple hash functions allows multiple score managers for each host to provide redundancy. , GossipTrust [300]) relying on a similar method are proposed, to look at different aspects of the problem.