Dynamic Selection of Web Services with Recommendation System
NWESP '05 Proceedings of the International Conference on Next Generation Web Services Practices
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
An Extensible and Personalized Approach to QoS-enabled Service Discovery
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
Improving Web Service Discovery with Usage Data
IEEE Software
Introduction to Information Retrieval
Introduction to Information Retrieval
A Personalized Approach to Experience-Aware Service Ranking and Selection
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Introducing Preferences over NFPs into Service Selection in SOA
Service-Oriented Computing - ICSOC 2007 Workshops
Personalized Web Service Ranking via User Group Combining Association Rule
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
CloudRank: A QoS-Driven Component Ranking Framework for Cloud Computing
SRDS '10 Proceedings of the 2010 29th IEEE Symposium on Reliable Distributed Systems
On the Social Aspects of Personalized Ranking for Web Services
HPCC '11 Proceedings of the 2011 IEEE International Conference on High Performance Computing and Communications
Collaborative Filtering Based Service Ranking Using Invocation Histories
ICWS '11 Proceedings of the 2011 IEEE International Conference on Web Services
A model of user preferences for semantic services discovery and ranking
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
| Hi-index | 0.00 |
Modeling users' online behavior has great benefit for many e-Commerce web sites and search engines. In the context of software service selection, if we could understand users' personal preferences, we could rank the services in a more satisfactory way. Many users have some general preferences on the desired values of non-functional properties (e.g. provider history, service popularity, etc.) of services, even if they may not explicitly define them. In this paper, we propose to build user profiles on these non-functional preferences, and then use them to personalize the ranking results for individual users. Our experiment showed that personalized ranking could promote the services matching with the user preferred non-functional values to higher positions, making it easier for users to identify their desired services. We also tested how different factors impact the degree of improvement on the ranking accuracy.