Title: Research on e-business requirement information resource extraction method in network big data
Authors: Yawen Li
Addresses: School of Management Engineering, Anhui Polytechnic University, Wuhu, 241000, China
Abstract: For the challenge of the data sparsity of user-behaviour in the current e-business personalised recommendation system, an information resource extraction method for e-business requirements based on similar case analysis is proposed in this paper. A recommendation model for e-commerce users' requirements information resources is built. the method based on similar case analysis is introduced into the personalised recommendation of e-business under the background of the personalised recommendation of e-business considering the potential requirement. The feature attribute similarity and comprehensive similarity of customer registration information are calculated. Combining user preferences, e-business resources from users' requirements in the case set are extracted. Experimental results show that the proposed method has good effect on product coverage, product exposure rate, and feedback rate. It can overcome the behaviour sparsity of user-product, and extract the dark information in e-business requirement information resources, and overcome the long tail recommendation.
Keywords: network big data; e-business; requirement information resources; extraction method; similar case analysis; A recommendation model; user preferences.
International Journal of Autonomous and Adaptive Communications Systems, 2023 Vol.16 No.2, pp.188 - 202
Received: 20 Jan 2020
Accepted: 31 Aug 2020
Published online: 24 May 2023 *