This article was first published on Stories by Monetha on Medium
In this article, we introduce our approach to reputation scoring and how we use subjective logic to calculate individualized ratings based on a network of experiences.
Reputation scoring background
Reputation scoring is widely used in many domains to measure the trustworthiness of a target (a user or service) based on information about it and its past behavior.
Users observe the events that immediately followed the target’s behavior, which determines how they perceive the experience. Events depend on a context and can be, for instance, voiced opinions, transactions, or signed documents. Thus, the reputation of a target is a collective measure of trustworthiness built from users’ experiences.
With reputation systems, users can rate the target, expressing their direct judgements. Other users can then consider the resulting rating and decide whether to interact with the target. This is one of the primary ways of how we build trust in the digital world.
Generic reputation systems vs. our approach
Today’s reputation systems generally constrain themselves to an average of aggregate ratings about a target. The result is an absolute representation of users’ experiences. Five stars for me are five stars for everyone else as well.
Our system, on the other hand, takes into account the network of relations a user has leading to the target. Each of their opinions can have a different weight depending on their proximity, experience, and other factors. Therefore, two buyers are likely to have a different impression of the seller’s trustworthiness if their network of relations differs in that particular context.
This allows us to more accurately emulate the trust relations found in the physical world, only in the digital domain and at a much larger scale.
Scoring models we use
The Monetha Platform uses several models to aggregate trust information and compute a score, namely flow-based reputation models and Subjective Logic. Flow-based ...
To keep reading, please go to the original article at:
Stories by Monetha on Medium