Web Service Recommendation via Exploiting Location and QoS Information
- 13 December 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Parallel and Distributed Systems
- Vol. 25 (7), 1913-1924
- https://doi.org/10.1109/tpds.2013.308
Abstract
Web services are integrated software components for the support of interoperable machine-to-machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems (e.g., ill-suited performance) to the resulting applications. In this paper, we propose a novel collaborative filtering-based Web service recommender system to help users select services with optimal Quality-of-Service (QoS) performance. Our recommender system employs the location information and QoS values to cluster users and services, and makes personalized service recommendation for users based on the clustering results. Compared with existing service recommendation methods, our approach achieves considerable improvement on the recommendation accuracy. Comprehensive experiments are conducted involving more than 1.5 million QoS records of real-world Web services to demonstrate the effectiveness of our approach.Keywords
This publication has 30 references indexed in Scilit:
- QoS Ranking Prediction for Cloud ServicesIEEE Transactions on Parallel and Distributed Systems, 2012
- Decision Tree Learning from Incomplete QoS to Bootstrap Service RecommendationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- A Clustering-Based QoS Prediction Approach for Web Service RecommendationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix FactorizationIEEE Transactions on Services Computing, 2012
- QoS-Aware Web Service Recommendation by Collaborative FilteringIEEE Transactions on Services Computing, 2011
- TQoS: Transactional and QoS-Aware Selection Algorithm for Automatic Web Service CompositionIEEE Transactions on Services Computing, 2010
- A Fine-Grained Reputation System for Reliable Service Selection in Peer-to-Peer NetworksIEEE Transactions on Parallel and Distributed Systems, 2007
- Scalable collaborative filtering using cluster-based smoothingPublished by Association for Computing Machinery (ACM) ,2005
- Collaborative filtering via gaussian probabilistic latent semantic analysisPublished by Association for Computing Machinery (ACM) ,2003
- An algorithmic framework for performing collaborative filteringPublished by Association for Computing Machinery (ACM) ,1999