Improving Resource Matchmaking through Feedback Integration

Distributed systems in which users consume and supply different types of services and resources are becoming ever more prevalent. For the matching of consumers and providers, preference-based two-sided matching algorithms can be used to improve the efficiency of the overall match outcome. In such systems, requesting (providing) users rank others based on preferences derived from feedback. Trust can be inferred using trust networks through direct or indirect feedback based on previous actions, and influences the preference ranking that users submit to the matching algorithm. As feedback influences the preference ranking and thus the matching, in this paper we propose a novel methodology which makes use of feedback in the decision making of preference rankings, in order to avoid being matched to untrustworthy users (or provides not likely to deliver their service). We use a simulation based validation approach to determine the effects of dynamic/continuous feedback and its influence on preference ranking of providers.