IDSS: an intelligent decision support system for e-purchasing using CBR and CF

摘要

Procurement holds significant business value, as most organisations spend at least one-third of their overall budget on purchasing goods and services. In this paper, we describe the design and implementation of an Intelligent Decision Support System (IDSS) for electronic purchasing. The theory of purchasing decision traditionally assumes that the offered quantity and quality are fixed prior to source selection. Decision Support Systems (DSS) are the need of the hour to assure results at a faster rate that best match the buyers' preferences and give valid recommendations. A two-dimensional approach is proposed: first, the Case-Based Reasoning (CBR) approach is used, which is a novel paradigm that solves a new problem by remembering a previous similar situation and reusing the information on and knowledge of that situation to bring out similar cases at a faster rate, depending on the predetermined similarity criteria. Second, the slope one predictors for online rating-based Collaborative Filtering (CF) and item-to-item CF are combined to produce accurate recommendations on the basis of the resources rating given by the users to help each other find better content. Our approach is database-driven, which provides buyers with more flexibility in the specification of their purchasing request and allows for an efficient information exchange among the participants. Finally, the purchaser can improve his profit.