Publication Date


Document Type


Committee Members

Keke Chen (Committee Member), Meilin Liu (Committee Member), Krishnaprasad Thirunarayan (Advisor)

Degree Name

Master of Science (MS)


World Wide Web has grown rapidly in the last two decades with user generated content and interactions. Trust plays an important role in providing personalized content recommendations and in improving our confidence in various online interactions. We review trust propagation models in the context of social networks, semantic web, and recommender systems. With an objective to make trust propagation models more flexible, we propose several extensions to the trust propagation models that can be implemented as configurable parameters in the system. We implement Local Partial Order Trust (LPOT) model that considers trust as well as distrust ratings and perform evaluation on dataset to demonstrate the improvement in recommendations obtained by incorporating trust models. We also evaluate in terms of performance of trust propagation models and motivate the need for scalable solution. In addition to variety, real world applications need to deal with volume and velocity of data. Hence, scalability and performance are extremely important. We review techniques for large-scale graph processing, and propose distributed trust aware recommender architectures that can be selected based on application needs. We develop distributed local partial order trust model compatible with Pregel (a system for large-scale graph processing), and implement it using Apache Giraph on a Hadoop cluster. This model computes trust inference ratings for all users accessible within configured depth from all other users in the network in parallel. We provide experimental results illustrating the scalability of this model with number of nodes in the cluster as well as the network size. This enables applications operating on large-scale to integrate with trust propagation models.

Page Count


Department or Program

Department of Computer Science

Year Degree Awarded