To enhance the long-term holding value of DREP DID, DREP launched a reputation system. This consists of a general reputation protocol, reputation pipeline interface, reputation data storing and algorithm library, reputation incentive mechanism, reputation account management and fake account identification mechanism, etc.
1. An Overview of The Reputation System¶
DREP Reputation System is a comprehensive close-loop ecosystem which includes a general reputation protocol, reputation pipeline interface, reputation on-chain data storage and algorithm library, reputation reward system, reputation value account management and fake account identification mechanisms. In the ecosystem, users’ behavior is linked to their reputation which will be evaluated by multiple interacting parties. Users will also receive complete real-time updates on their reputation.
2. Advantages of DREP Reputation System Universality¶
Universality The Reputation Protocol is not designed for any single platform, but caters to different industries with different templates featuring cross-platform and data integration. User loyalty can be obtained only by adjusting the details according to the industry/platform. Eventually, users may opt to share data within their own scope, thus reducing the cost of data acquisition.
Reputation decays slowly in the early stages and quickly over time, rapidly plunging to a lower value in the later stages. The next time this user activates the same app, the reputation value will commence from the lower value and not start from 0.
Closed-loop Ecosystem The DREP Reputation Protocol is not only of great importance for enterprise clients but also beneficial for individual users. Through reputation collection, it is able to offer customized preferential treatment, further promoting engagement and consumption, thus forming a closed-loop ecosystem.
3. Reputation System Complements and Enhances ID¶
The DREP Reputation Protocol does more than supplement, enrich and replace the traditional points system. The ecosystem of different applications will eventually collectively contribute to the users’ reputation image. Obtaining information from one application is insufficient to portray a user’s complete image and interests. To facilitate targeted recommendation and marketing, various data points such as reputation image summarized from multiple applications as well as user’s interest and habits extracted through big data analysis could be utilized. This method would be comprehensive and avoids privacy infringement.