# Two-Sided Matching Decision Method of Electricity Sales Package Based on Disappointment Theory
## 1 Introduction
The authors of [6], on the basis of the collaborative recommendation algorithm, introduced a recommendation method for electricity sales package in the Spark environment, comprehensively considered the electric power customers and electricity sales package volume for prediction and scoring, and obtained the recommendation data; The authors of [12] designed a recommendation system for electricity sales packages based on collaborative filtering and the hybrid Bayesian algorithm according to the electricity consumption characteristics of electric power customers. The authors of [13], on the basis of the artificial intelligence technology of collaborative filtering, recommended electricity sales packages according to the energy consumption characteristics of intelligent building customers. Although the indirect recommendation method is widely used, because the collaborative filtering method needs to cluster users, it needs to set the number of clusters in advance, which leads to low accuracy and efficiency of clustering and reduces the accuracy of the method. Since the model is based on historical data, the recommendation performance of the model may also be limited when there is a lack of historical data, or when the data were collected too long ago. The direct recommendation method is mainly applied through the use of iSelect [14], Check24 [15], and other power-selling package recommendation platforms. iSelect is an Australian company that provides comparison services for various products and services, including insurance, electricity, and gas providers, broadband plans, and financial products. It helps consumers compare different options and choose the best one according to their needs and preferences. Check24 is a German online comparison website that allows consumers to compare prices and services for a wide range of products and services. Check24 aims to provide transparency and help customers make informed decisions by offering comparisons, reviews, and user ratings. However, this method is mainly based on the power-selling price and lacks many key factors that have an important impact on the electricity price; thus, the recommendation results are not accurate. In addition, with regard to the two major categories of recommendation methods mentioned above, when the power user and the electricity sales package evaluate each other, they do not take into account the situation that the power user may not know about the additional services of the electricity sales package, and that the electricity sales company may not know about the preferences of the power user. However, in the actual situation, due to various factors such as the source of information and the large number of electricity sales packages, it is often difficult for the power user to thoroughly understand the specific information of various electricity sales packages. Therefore, how electric power customers make the best choice under limited cognition is an urgent problem to be settled. To sum up, this paper proposes a decision-making method for a user's electricity sales package considering an incomplete fuzzy preference relationship. This method can take the subjective psychological feelings into account, overcome the limitations of limited knowledge or understanding between the matching parties, and still accurately determine the optimal matching scheme under certain conditions of missing information.