# Reputation Bootstrapping for Composite Services using CP-nets

arXiv: https://arxiv.org/abs/2105.15135 · HTML: https://mlsanity.com/paper/2105.15135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 4 of 4)

- [Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations](https://mlsanity.com/paper/1804.04577/) (2018)
- [AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes](https://mlsanity.com/paper/2008.12987/) (2020)
- [Predicting the Popularity of GitHub Repositories](https://mlsanity.com/paper/1607.04342/) (2016)
- [Just-in-Time Memoryless Trust for Crowdsourced IoT Services](https://mlsanity.com/paper/2005.14413/) (2020)

## Abstract (arXiv)

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.