Personalized QoS Prediction forWeb Services via Collaborative Filtering

TLDR

Quality of Service (QoS) attributes are increasingly considered in web service selection, and QoS prediction typically relies on other consumers' experiences. The study aims to enable consumers to predict the QoS of unused web services prior to selection. The authors propose a collaborative filtering method that mines similarity among consumers' QoS experiences to generate predictions. Experimental results show that this approach significantly improves QoS prediction effectiveness for web services.

Abstract

Many researchers propose that, not only functional but also non-functional properties, also known as quality of service (QoS), should be taken into consideration when consumers select services. Consumers need to make prediction on quality of unused web services before selecting. Usually, this prediction is based on other consumers' experiences. Being aware of different QoS experiences of consumers, this paper proposes a collaborative filtering based approach to making similarity mining and prediction from consumers' experiences. Experimental results demonstrate that this approach can make significant improvement on the effectiveness of QoS prediction for web services.