Minimizing time risk in on-line bidding: An adaptive information retrieval based approach

Knowledge is of prime importance, particularly for the individuals who are involved in e-business. A lot of energy and time is wasted by the individuals in seeking required knowledge and information. In order to facilitate the individuals with required information, an efficient technique for the proper retrieval of knowledge is must. Almost all online business activities, particularly e-auction based firms are surrounded by various risk factors pertaining to time, security, brand etc. The main focus of the present paper is to analyze all such risk factors and further to categorize the same as per their degree of influence. A nominal group technique (NGT) based approach has been utilized to do the same that ranks the risk factors using agreed criteria based approach. Further, the paper proposed an adaptive information retrieval to resolve the problems related to time risk in online bidding process, while other risk factors has been tried to resolved by using corporate memory based data warehousing. Efficient knowledge retrieval along with the knowledge development and knowledge management became a backbreaking task for any organization. A corporate memory based approach has been utilized to represent the required knowledge stored in memory warehouse for its current and future usage. In underlying retrieval model, adaptiveness is achieved using genetic algorithm based matching function adaptation, where, a total of five matching functions viz. Jaccard’s coefficient, Overlap’s coefficient, Dice coefficient, Inclusion measure, and Cosine measures have been considered to determine the retrieval effectiveness. Later, effectiveness of information retrieval system is calculated in terms of well known parameters namely precision, recall, fallout and miss. Results of adaptive information retrieval using a weighted combination of matching functions are compared with individual matching functions.

Research highlights► Nominal group technique to rate e-bidding risk factors.► Corporate memory approach to maintain bidding database. ► Genetic algorithm to optimize retrieval performance.