User-Characteristic-Oriented Bilateral Matching between Online Learning Service Demanders and Providers

Abstract

Under the background of information technology and the Internet era, the matching problem in different application scenarios is becoming increasingly prominent. With respect to the matching problem in knowledge services, enabling users to choose suitable knowledge out of massive information has become an urgent demand to be satisfied. Initiating from interdisciplinary perspective, this paper proposes a matching method for online learning services according to user characteristics, which focuses on the matching of decision making for knowledge service with user relevance and characteristic under social network environment. Firstly, the complex network among users is constructed, and the user group is subcategorized into subgroups, thereby aggregating the subgroup information effectively. Secondly, the weight of the indices that evaluate the matching subject is determined by conducting the best-worst method. Thirdly, considering the difference between the expectation and actual levels of the matching subject, the cumulative prospect theory is adopted to calculate the satisfaction degree of both sides. Aiming at maximizing the satisfaction degree of the subjects, a multi-objective optimization model is established to obtain the optimal matching pairs. Finally, the validity and rationality of the proposed method are verified, offering interdisciplinary perspective and theoretical foundation for knowledge service matching and the education reform of humanities.