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In order to facilitate human decision making, trust evaluation has received widespread attention in many fields, especially for online services. Most of the existing methods consider trust in a person as a value which does not vary across different scenarios without any attention to the distinction of domains or communities where trust is derived. However, the notion of context is a significant and indispensable factor for trust evaluation in practice. Due to the lack of the consideration of context, traditional methods cannot resolve the issue that arises when a highly trustworthy person in one domain is likely to dominate the results of trust assessment in others where the person is in fact less authoritative. To solve this problem, in this paper, we develop a general approach to accomplish topic-sensitive trust evaluation by considering the context of trust. We first propose a general framework which presents the well-organized architecture of topic-sensitive trust evaluation in online communities. Then, a user-topic model is proposed to automatically extract topic data from user-generated content based on the Labeled Latent Dirichlet Allocation (LLDA) model. To compare the topic differences between users, we design a topic coverage function for revealing their trust relationships in diverse topics. Moreover, we employ two traditional methods and extend them to accomplish trust prediction for people with multiple domain knowledge. Experiments based on a real-world dataset show that extended topic-sensitive approaches are more adaptive and accurate than those topic-free trust evaluation approaches, especially when the trust application scenario features multiple topics.
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- Labeled LDA
- Topic-sensitive analysis
- Trust evaluation
- Trust propagation
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