Research On E-barter Resource Matching Model With Maximizing User Utility

With the development of Internet technology,barter,which is an ancient way of trading,has gradually developed into a modern barter trade—electronic barter.Ebarter does not use cash,through the network platform to allow users to exchange goods,which in today's inventory accumulation,the increasing availability of idle items,is of great significance to both the enterprise and individuals.E-barter has the same features as e-commerce,but because no currency is used and the type and quantity of resources in the bartering resource pool become larger,resource matching has become a key factor in the success of e-barter.The e-barter resource matching problem can be solved by graph theory to establish a network model.However,there may be multiple bartering programs,which is difficult to ensure the maximization of user utility and bring choice problems to bartering decisions.The current era of the Internet is the era of users.E-commerce business model emphasizes user-centric principles.E-barter also faces the situation of relying on the Internet,while traditional e-barter resource matching models are mostly oriented to the barter platform,which is no longer applicable to the current user-centric business model.The shift from benefit to user-oriented utility is necessary.Aiming at the above problems,the e-barter resource matching model with maximizing user utility is established.User preference is used to express the user's bartering expectation.The user utility function is established to solve the selection problem of multiple bartering schemes,so as to achieve the optimal matching.The main research work of this paper is as follows:(1)An e-barter resource matching method based on preference order to maximize user utility.The research in this area is divided into e-barter resource matching methods that maximize user utility of 1: 1 and m: n.First,the user's preference order problem is described in the barter scenario,and the preference relationship is converted into preference order.The user utility in the preference order case is aggregated,and then a user utility maximization model based on the preference order is constructed,and finally the model is solved to obtain the matching result of the user utility maximization.(2)An e-barter resource matching method based on multi-index user utility maximization.The user's needs are diverse.When users have multiple types or more than one item,the demand for items will also be complicated.At this time,a multi-index ebarter resource matching model needs to be constructed to achieve the maximum user utility.This method first designs the index attributes of the e-barter market,and uses the importance ranking method and the binomial coefficient method to determine the weight of each indicator,and then uses the expected indicator as a reference point,and maximize the user's utility and the maximum number of exchanges as the goals to build a 1: 1 singleobjective optimization model and a m: n-based dual-objective optimization model respectively to achieve the optimal resource matching.