Reputation and recommender systems have widespread use in online marketing, web services, P2P computing, e-commerce, social settings, and education. The objective of this research is to develop reliable, scalable and dependable schemes that are also resilient to malicious behaviors.

The approach is based on viewing both reputation and recommender systems as solving for marginal probability distribution functions from complicated global (joint-distribution) functions of many variables (users, items or service providers, and ratings). These marginal probability distributions are functions of the variables representing the reputation values (in reputation systems) and the ratings to be predicted to the users (in recommender systems). However, computing these marginal distributions are computationally prohibitive (i.e., exponential with the number of variables) for large scale reputation and recommender systems. Therefore, this research represents the reputation and recommender systems using factor graphs or Pairwise Markov Random Fields, and utilizes the Belief Propagation (BP) algorithm to efficiently (in linear complexity) solve for these marginal distributions. In particular, the project includes research to: (1) study the general theories of BP-based reputation management and recommender systems on various graphical models and develop novel algorithms; (2) study the convergence, scalability, and robustness of the developed algorithms via mathematical analysis and intensive simulations; (3) develop a Belief Propagation based Iterative Trust and Reputation Management (BP-ITRM) system and compare it with the current state of the art using real-life datasets and conducting user studies; and (4) adaptively learn various attack strategies against the reputation and recommender systems, determine the impacts of such attacks, and decrease their impact.

The project is expected to make contributions to both theory and practice by developing a new reputation management framework for recommender systems, and algorithms that provide effective ways to deal with information overload and access to relevant information. It is anticipated that the work will drive the technology for effective online products and information services. Technologies resulting from this research will bring a broad range of benefits in many areas including online services, P2P and distributed computing systems, e-commerce, business, social settings, education, national security and the economy. The research results are expected to make theoretical contributions relevant to computer science, information theory and statistical inference. This project offers a unique opportunity to train graduate students and expose undergraduate students to cross-cutting research in different fields (computer science, information theory, statistical inference). The project website ( is used to disseminate resulting publications, datasets (obtained from user studies and mathematical models), and course materials to broad communities of researchers, students and industry practitioners.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Maria Zemankova
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Georgia Tech Research Corporation
United States
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