Recommender and Reputation Systems: Principles and Practices

推荐和信誉系统处理过去用户的评级，以指导当前用户的行为。这样的系统为研究大规模社会现象提供了一个天然的实验室，包括公共产品的提供(评级本身)，以及声誉作为信任和可信度调节器的作用。本项目将专注于两个广泛使用的系统，eBay和Slashdot。将深入评估这两个站点提供的使用日志。这一分析应该会揭示围绕评级提供出现的社会实践，以及评级实际上如何指导用户决定购买什么，从谁那里购买，以及阅读哪些信息。激励的博弈论分析将与实证分析相结合，以指导潜在的制度改进设计。这项研究将提高数百万人已经在互动的网站的功能和公众理解。此外，它还将提供对非人格化社会资本运作的基本见解，即在仍然陌生的人之间出现的富有成效的社会关系，而不是形成长期关系网络。

Recommender and reputation systems process ratings from past users to guide current users' behavior. Such systems provide a natural laboratory for the study of large-scale social phenomena, including the provision of public goods (the ratings themselves), and the role of reputation as a regulator of trust and trustworthiness.This project will focus on two widely used systems, eBay and Slashdot. Usage logs provided by the two sites will be assessed in depth. This analysis should reveal the social practices that have emerged surrounding the provision of ratings, and the ways ratings actually guide users' decisions about what to buy and from whom, and what messages to read. Game-theoretic analyses of incentives will combine with the empirical analysis to guide the design of potential system improvements.This research should improve the functioning and public understanding of web sites where millions of people already interact with each other. In addition, it will provide fundamental insights into the workings of impersonal social capital, i.e., productive social relations that emerge among people who remain strangers rather than forming networks of long-term relationships.