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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 46403 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Jalili, Mahdi
- Abstract:
- Trust prediction, the ability to identify how much to trust to allocate an unknown user, is an important prerequisite toward the development of scalable on-line e-commerce communities. We are more likely to purchase an item from a seller on an e-commerce websites such as eBay or Amazon, if our trusted acquaintances have reported positive experiences with that seller in the past. Reviews from trusted users will carry more weight towards the purchasing decision than reviews from anonymous or unknown customers. Thus, these platforms must support computational mechanisms for propagating trust between users. One of the significant challenges in the trust prediction problem is the unprecedented growth in the amount of online interactions and the sparsity in the amount of known (labeled) relationships which has made the problem of predicting user trust relationships critically important and posed a significant challenge to the usage of machine learning techniques. This study presents two semi-supervised approaches for predicting trust between users. The first one is a community detection approach which leverages the network of available trust relations and rating similarities to compensate for the lack of labels. The key insight behind this framework is that trust values from the central community members can be used as a predictor for relationships between other community members. Here we evaluate the usage of two community detection algorithms, one of which works merely on the trust network while the other one uses both. The second approach takes an advantage of fuzzy logic and ANFIS due to the fact that trust prediction problem is inherently a fuzzy concept. Also, this approach uses predefined features such as trustor’s optimistism, trustee’s integrity and similarity between these features. We evaluate the proposed algoithms and show that they outperform other existing trust prediction methods on datasets from the well-known product review websites Epinions and Ciao
- Keywords:
- Social Networks ; Trust ; Trust Value Prediction ; User-Item Interaction
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