Hybrid recommender systems for personalized government-to-business e-services
- Publication Type:
- Thesis
- Issue Date:
- 2012
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As e-Governments around the world face growing pressures to improve the quality of
service delivery and become more efficient and cost-effective, their initiatives
currently focus on providing users with a seamless service delivery experience. Webbased
technologies offer governments more efficient and effective means than
traditional physical channels to provide high quality e-Service delivery to their users,
which include citizens and businesses. Government-to-Business (G2B) e-Services
involve information distribution, transactions, and interactions with businesses m
varying ways via e-Government websites and portals. The G2B e-Services aim to
reduce burdens on businesses and to provide effective and efficient access to
information for business users. One of the most important e-Services of G2B is the
promotion of local businesses goods and services to consumers (i.e., local and
overseas businesses) by providing on line business directories. However, with the
rapid growth of information and unreliable search facilities, busine s users, who are
seeking 'one-to-one' e-Services from government in highly competitive markets,
struggle with online business directories and increasingly find it difficult to locate
business pa1tners according to their needs and interests. How, then, can business users
be provided with inforn1ation and services specific to their needs, rather than an
undifferentiated mass of information? An effective solution proposed in this research
is the development of personalized G2B e-Services using recommender systems. It is
worth mentioning that the adoption of recommender systems in the context of e-
Government to provide personalized services has received very limited attention in
the literature.
Recommender systems aim to suggest the right items (products, services or
information) that best match the needs and interests of particular users based on their
explicit and implicit preferences. In current recommender systems, the Collaborative
Filtering (CF) approaches are the most popular and widely adopted recommendation
approaches. Regardless of the success of CF-based approaches in various
recommendation applications, they still suffer from data uncertainty, data sparsity,
cold-start item and cold-start user problems, resulting in poor recommendation
accuracy and reduced coverage. An effective solution proposed in this research to
alleviate such problems is the development of hybrid and fusion-based
recommendation algorithms that exploit and incorporate additional knowledge about
users and items. Such knowledge can be extracted from either the users ' trust social
network or the items' semantic domain knowledge.
This research explores the adoption of recommender systems m an e-
Govemment context for the provision of personalized G2B e-Services. Accordingly, a
G2B recommendation framework for providing personalized G2B e-Services
(particularly personalized business partner recommendations) for Small-to-Medium
Businesses (SMBs) is proposed. Novel hybrid and fusion-based recommendation
models and algorithms are also proposed and developed to overcome the limitations
of existing CF-based recommendation approaches. Experimental results on real
datasets show that our proposed recommendation algorithms significantly outperfmm
existing recommendation algorithms in terms of recommendation accuracy and
coverage when dealing with data sparsity, cold-start item and cold-start user
limitations inherent in CF-based recommendation approaches.
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