Recommendation in an Evolving Service Ecosystem Based on Network Prediction
- 20 January 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automation Science and Engineering
- Vol. 11 (3), 906-920
- https://doi.org/10.1109/tase.2013.2297026
Abstract
Service computing plays a critical role in business automation and we can observe a rapid increase of web services and their compositions nowadays. Web services, their compositions, providers, consumers, and other entities such as context information, collectively form an evolving service ecosystem. Many service recommendation methods have been proposed to facilitate the use of services. However, existing approaches are mostly based on all-time statistics of usage patterns, and overlook the temporal aspect, i.e., the evolution of the ecosystem. As a result, recommendation may consist of obsolete services and also does not reflect the latest trend in the ecosystem. In order to overcome this limitation, we propose an innovative three-phase network prediction approach (NPA) for evolution-aware recommendation. First, we introduce a network series model to formalize the evolution of the service ecosystem and then develop a network analysis method to study the usage pattern with a special focus on its temporal evolution. Afterward a novel service network prediction method based on rank aggregation is proposed to predict the evolution of the network. Finally, using the network prediction model, we present how to recommend potential compositions, top services and service chains, respectively. Experiments on the real-world ProgrammableWeb data set show that our method achieves a superior performance in service recommendation, compared with those that are agnostic to the evolution of a service ecosystem.Keywords
Funding Information
- National Natural Science Foundation of China (61174169)
- National Key Technology R&D Program (2012BAF15G01)
This publication has 43 references indexed in Scilit:
- A Semantic Bayesian Network for Web Mashup Network ConstructionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Cold start link predictionPublished by Association for Computing Machinery (ACM) ,2010
- The WEKA data mining softwareACM SIGKDD Explorations Newsletter, 2009
- The Time-Series Link Prediction Problem with Applications in Communication SurveillanceINFORMS Journal on Computing, 2009
- An introduction to ROC analysisPattern Recognition Letters, 2005
- Uncovering the overlapping community structure of complex networks in nature and societyNature, 2005
- Friends and neighbors on the WebSocial Networks, 2003
- Models for metasearchPublished by Association for Computing Machinery (ACM) ,2001
- Rank aggregation methods for the WebPublished by Association for Computing Machinery (ACM) ,2001
- The use of the area under the ROC curve in the evaluation of machine learning algorithmsPattern Recognition, 1997