Trust- and Distrust-Based Recommendations for Controversial Reviews

Recommender systems that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional collaborative filtering systems, provided they succeed in utilizing the additional trust and distrust information to their advantage. We compare the performance of several well-known trust-enhanced techniques for recommending controversial reviews from Epinions.com, and provide the first experimental study of using distrust in the recommendation process

The Economic Case for Cyberinsurance

We present three economic arguments for cyberinsurance. First, cyberinsurance results in higher security investment, increasing the level of safety for information technology (IT) infrastructure. Second, cyberinsurance facilitates standards for best practices as cyberinsurers seek benchmark security levels for risk management decision-making. Third, the creation of an IT security insurance market redresses IT security market failure resulting in higher overall societal welfare. We conclude that this is a significant theoretical foundation, in addition to market-based evidence, to support the assertion that cyberinsurance is the preferred market solution to managing IT security risks.

The effects of provider control of Blue Shield plans : regulatory options / BEBR No. 645

Title page includes summary.Includes bibliographical references (p. 30-32)

Learning Personalized Risk Preferences for Recommendation

The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions. With this information, they can infer the quality of products to reduce the risk of purchase. Specifically, items with high rating scores and good reviews tend to be less risky, while items with low rating scores and bad reviews might be risky to purchase. On the other hand, the purchase behaviors will also be influenced by consumers' tolerance of risks, known as the risk attitudes. Economists have studied risk attitudes for decades. These studies reveal that people are not always rational enough when making decisions, and their risk attitudes may vary in different circumstances. Most existing works over recommendation systems do not consider users' risk attitudes in modeling, which may lead to inappropriate recommendations to users. For example, suggesting a risky item to a risk-averse person or a conservative item to a risk-seeking person may result in the reduction of user experience. In this paper, we propose a novel risk-aware recommendation framework that integrates machine learning and behavioral economics to uncover the risk mechanism behind users' purchasing behaviors. Concretely, we first develop statistical methods to estimate the risk distribution of each item and then draw the Nobel-award winning Prospect Theory into our model to learn how users choose from probabilistic alternatives that involve risks, where the probabilities of the outcomes are uncertain. Experiments on several e-commerce datasets demonstrate that our approach can achieve better performance than many classical recommendation approaches, and further analyses also verify the advantages of risk-aware recommendation beyond accuracy