  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.
