Reputation system

Reputation systems are programs or algorithms that allow users to rate each other in online communities in order to build trust through reputation. Some common uses of these systems can be found on E-commerce websites such as eBay, Amazon.com, and Etsy as well as online advice communities such as Stack Exchange. These reputation systems represent a significant trend in "decision support for Internet mediated service provisions". With the popularity of online communities for shopping, advice, and exchange of other important information, reputation systems are becoming vitally important to the online experience. The idea of reputation systems is that even if the consumer can't physically try a product or service, or see the person providing information, that they can be confident in the outcome of the exchange through trust built by recommender systems. Collaborative filtering, used most commonly in recommender systems, are related to reputation systems in that they both collect ratings from members of a community. The core difference between reputation systems and collaborative filtering is the ways in which they use user feedback. In collaborative filtering, the goal is to find similarities between users in order to recommend products to customers. The role of reputation systems, in contrast, is to gather a collective opinion in order to build trust between users of an online community. Howard Rheingold states that online reputation systems are "computer-based technologies that make it possible to manipulate in new and powerful ways an old and essential human trait". Rheingold says that these systems arose as a result of the need for Internet users to gain trust in the individuals they transact with online. The trait he notes in human groups is that social functions such as gossip "keeps us up to date on who to trust, who other people trust, who is important, and who decides who is important".