The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking “one‐to‐one” e‐services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e‐services. Recommender systems is an effective approach for the implementation of Personalized E‐Service which has gained wide exposure in e‐commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item‐based fuzzy semantic similarity and item‐based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF‐based recommendation approaches, namely sparsity and new “cold start” item problems.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e-services, in particular, business partner recommendation e-services for Australian small to medium enterprises (SMEs). Design/methodology/approach -The study first proposes a product semantic relevance model. It then develops a hybrid semantic recommendation approach which combines item-based collaborative filtering (CF) similarity and item-based semantic similarity techniques. This hybrid approach is implemented into an intelligent business-partner-locator recommendation-system prototype called BizSeeker. Findings -The hybrid semantic recommendation approach can help overcome the limitations of existing recommendation techniques. The recommendation system prototype, BizSeeker, can recommend relevant business partners to individual business users (e.g. exporters), which therefore will reduce the time, cost and risk of businesses involved in entering local and international markets. Practical implications -The study would be of great value in e-government personalization research. It would facilitate the transformation of the current G2B e-services into a new stage wherein the e-government agencies offer personalized e-services to business users. The study would help government policy decision-makers to increase the adoption of e-government services. Originality/value -Providing personalized e-services by e-government can be seen as an evolution of the intentions-based approach and will be one of the next directions of government e-services. This paper develops a new recommender approach and systems to improve personalization of government e-services.