A Collective Intelligence Based Business-Matching and Recommending System for Next Generation E-Marketplaces

Abstract

The present work proposes and describes a novel approach to business-matching and recommending systems, for the next-generation e-marketplaces, with a focus on the needs and realities of small business. The proposed matching system makes use of Collective Intelligence (IC) means to identify and recommend the best matching business opportunities for the user. At first, the content of business proposals and companies’ profiles are parsed and indexed. The search engine starts by expanding the keyword of interest, to enable an intuitive-like search. The look-up results are then filtered based on a compatibility scoring mechanism. The customized business-matching results and recommendations are later served to the user via a novel visual interactive graphical interface. A prototype system applying the proposed and described approach is currently being developed and experimentally tested, to fully demonstrate the capabilities of the proposed system on real-world data. Although the prototype is at an early stage, the initial experiments show promising results.

Keywords: Proposals, Information systems, Databases, Artificial intelligence, Prototypes, Companies