| Seminar on Trust and Reputation Systems
Date: July 9, 2012 Speaker: Prof Audun Josang Abstract: The purpose of trust and reputation systems is to strengthen the quality of markets and communities by providing an incentive for good behaviour and quality services, and by sanctioning bad behaviour and low quality services. However, trust and reputation systems will only be able to produce this effect when they are sufficiently robust against strategic manipulation or direct attacks. Currently, very few practical trust and reputation systems can be characterised as robust. In order to set robustness requirements it is important to know how important robustness really is in a particular online community or market. This presentation discusses the need for robustness in trust and reputation systems, and the research challenges for developing sound principles and mechanisms for achieving adequate robustness. Bio: The presenter Dr. Audun Josang is Professor at the University of Oslo, and Adjunct Professor at QUT (Queensland University of Technology) in Australia. Prof. Josang considered as one of the leading international experts in the rapidly growing area of trust and reputation systems. Subjective logic and multinomial Bayesian reputation systems developed by Prof. Josang are being applied in trust management by practical implementations and academic research worldwide. Prof. Josang is also an expert in information security. 3:00pm - 3:30pm: break 3:30pm - 4:00pm: Towards a Comprehensive Testbed to Evaluate the Robustness of Reputation Systems against Unfair Rating Attacks Speaker: Athirai A. Irissappane Abstract: Evaluation of the effectiveness and robustness of reputation systems is important for the trust research community. However, existing testbeds are mainly simulation based and not flexible to perform robustness evaluation, and none of them is specifically designed to evaluate the robustness of reputation systems against unfair rating attacks. In this work, we propose a novel comprehensive testbed by simulating three types of environments (simulated environments, real environments with simulated unfair rating attacks, and real environments with detected unfair ratings). The testbed incorporates sophisticated deception models and unfair rating attack models, and introduces several performance metrics to fully test and compare the effectiveness and robustness of different reputation systems. We also provide two case studies to demonstrate the usage of partial features of our proposed testbed. 4:00pm - 4:30pm: A Simple but Effective Method to Incorporate Trusted Neighbors in Recommender Systems Speaker: Guibing Guo Abstract: Providing high quality recommendations is important for online systems to assist users who face a vast number of choices in making effective selection decisions. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users. But it suffers from several issues like data sparsity and cold start. To address these issues, in this work, we propose a simple but effective method, namely "Merge", to incorporate social trust information (i.e. trusted neighbors explicitly specified by users) in providing recommendations. More specifically, ratings of a user's trusted neighbors are merged to represent the preference of the user and to find similar other users for generating recommendations. Experimental results based on three real data sets demonstrate that our method is more effective than other approaches, both in accuracy and coverage of recommendations. 4:30pm - 5:30pm: Discussions and meeting with Professor Audun Josang |