A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services
- 15 July 2011
- journal article
- research article
- Published by Hindawi Limited in International Journal of Intelligent Systems
- Vol. 26 (9), 814-843
- https://doi.org/10.1002/int.20495
Abstract
The information overload on the World Wide Web results in the underuse of some existing e-government services within the business domain. Small-to-medium businesses (SMBs), in particular, are seeking “one-to-one'' e-services from government in current highly competitive markets, and there is an imperative need to develop Web personalization techniques to provide business 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 on the problem of selecting a trustworthy business partner to perform reliable business transactions. In the business partner selection process, trust or reputation information is crucial and has significant influence on a business user's decision regarding whether or not to do business with other business entities. For this purpose, an intelligent trust-enhanced recommendation approach to provide personalized government-to-business (G2B) e-services, and in particular, business partner recommendation e-services for SMBs is proposed. Accordingly, in this paper, we develop (1) an implicit trust filtering recommendation approach and (2) an enhanced user-based collaborative filtering (CF) recommendation approach. To further exploit the advantages of the two proposed approaches, we develop (3) a hybrid trust-enhanced CF recommendation approach (TeCF) that integrates both the proposed implicit trust filtering and the enhanced user-based CF recommendation approaches. Empirical results demonstrate the effectiveness of the proposed approaches, especially the hybrid TeCF recommendation approach in terms of improving accuracy, as well as in dealing with very sparse data sets and cold-start users.This publication has 35 references indexed in Scilit:
- A Framework of Hybrid Recommendation System for Government-to-Business Personalized E-ServicesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- A Framework for an Ontology-based E-commerce Product Information Retrieval SystemJournal of Computers, 2009
- User Profile Modeling in the context of web-based learning management systemsJournal of Network and Computer Applications, 2008
- An ontology-based system for intelligent matching of travellers' needs for Group Package ToursInternational Journal of Digital Culture and Electronic Tourism, 2008
- Intelligent e-government services with personalized recommendation techniquesInternational Journal of Intelligent Systems, 2007
- Analysis and Classification of Multi-Criteria Recommender SystemsWorld Wide Web, 2007
- E-Government: Evolving relationship of citizens and government, domestic, and international developmentGovernment Information Quarterly, 2006
- Personalization technologiesCommunications of the ACM, 2005
- Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensionsIEEE Transactions on Knowledge and Data Engineering, 2005
- Fuzzy logic methods in recommender systemsFuzzy Sets and Systems, 2003