Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Personalization technologies: a process-oriented perspective
Communications of the ACM - The digital society
A Multi-Agent System for the management of E-Government Services
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Intelligent e-government services with personalized recommendation techniques: Research Articles
International Journal of Intelligent Systems
Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
A personalized recommendation system based on product taxonomy for one-to-one marketing online
Expert Systems with Applications: An International Journal
Hybrid web recommender systems
The adaptive web
A Framework of Hybrid Recommendation System for Government-to-Business Personalized E-Services
ITNG '10 Proceedings of the 2010 Seventh International Conference on Information Technology: New Generations
A fuzzy recommender system for eElections
EGOVIS'10 Proceedings of the First international conference on Electronic government and the information systems perspective
Semantic web techniques for personalization of egovernment services
CoMoGIS'06 Proceedings of the 2006 international conference on Advances in Conceptual Modeling: theory and practice
A trust-semantic fusion-based recommendation approach for e-business applications
Decision Support Systems
Hi-index | 0.00 |
The information overload problem results in the under-use of some existing e-Government services. Recommender systems have proven to be an effective solution to the information overload problem by providing users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-Governments can support businesses, which are seeking 'one-to-one' e-services, on the problem of finding adequate business partners. For this purpose, a Hybrid Semanticenhanced Collaborative Filtering (HSeCF) recommendation approach to provide personalized Government-to-Business (G2B) e-services, and in particular, business partner recommendation e-services for Small to Medium Businesses is proposed. Experimental results on two data sets, MovieLens and BizSeeker, show that the proposed HSeCF approach significantly outperforms the benchmark item-based CF algorithms, especially in dealing with sparsity or cold-start item problems.