Reputation Bootstrapping for Composite Services using CP-nets

TL;DR

This paper introduces a framework that uses CP-nets and Q-learning to bootstrap reputation in context-aware service compositions, addressing the lack of feedback and interdependence among services.

Contribution

It presents a novel method combining CP-nets and Q-learning to estimate reputation in complex service compositions considering topology and interdependence.

Findings

The approach effectively estimates reputation in composite services.

Experimental results demonstrate the efficiency of the method.

The framework accounts for reputation influence among component services.

Abstract

We propose a novel framework to bootstrap the reputation of on-demand service compositions. On-demand compositions are usually context-aware and have little or no direct consumer feedback. The reputation bootstrapping of single or atomic services does not consider the topology of the composition and relationships among reputation-related factors. We apply Conditional Preference Networks (CP-nets) of reputation-related factors for component services in a composition. The reputation of a composite service is bootstrapped by the composition of CP-nets. We consider the history of invocation among component services to determine reputation-interdependence in a composition. The composition rules are constructed using the composition topology and four types of reputation-influence among component services. A heuristic-based Q-learning approach is proposed to select the optimal set of reputation-related CP-nets. Experimental results prove the efficiency of the proposed approach.