Abstract
Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.
Highlights
In this rapidly changing information era, traditional government systems will not be able to meet the requirements of a new age sufficiently
Applicable methods should be proposed to meet above issues brought by E-government procurement (eGP) systems so as to achieve the optimal procurement scheme (OPS)
We propose a novel approach based on collaborative filtering and an extended Bayesian approach to assist the procurement sector in obtaining the OPS
Summary
In this rapidly changing information era, traditional government systems will not be able to meet the requirements of a new age sufficiently. Applicable methods should be proposed to meet above issues brought by eGP systems so as to achieve the optimal procurement scheme (OPS). The stateof-the-art literature has paid little attention to excogitating an available algorithm to achieve cost-saving, service level optimized, efficient, and effective procurement scheme. We propose a novel approach based on collaborative filtering and an extended Bayesian approach to assist the procurement sector in obtaining the OPS. A trapezoidal fuzzy number similarity algorithm is adopted to calculate the similarity between two services so as to extend the item-based collaborative filtering and our initial Bayesian approach [5]. Outline of the proposed algorithm in this paper will be summarized in Section 3 which contains three phases: data preparation stage, filtering stage, and search stage.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
