###### Abstract
Examples of such platforms include iSelect [12], Power to Choose [13], Check24 [14], Energy Made Easy [15], etc. Although the implementation of online recommendation platforms is simple and easy, this method only considers customers' electricity costs and ignores the diversity of customer evaluation information. For customers with complex electricity usage characteristics, there is still no suitable recommendation solution. Apart from the direct cost-based recommendation method, indirect recommendation methods for electricity sales packages mainly involve algorithm-based recommendations. Key research directions include collaborative filtering-based models [16; 17], matrix factorization-based models [18], deep learning-based models [19], etc. For example, reference [20] uses a fuzzy C-means clustering algorithm to classify customers, and based on the similarity between the target customer and historical customers in the same category, as well as historical customers' ratings of electricity sales packages, it predicts the ratings for the target customer to recommend the appropriate package. Reference [21] represents the electricity usage characteristics of residential customers using the energy consumption characteristics of household appliances and designs a recommendation system for residential electricity sales packages based on a Bayesian mixed collaborative filtering algorithm. In addition, reference [22] analyzes the impact of factors such as pricing methods, time-of-use pricing, average price, and the proportion of green electricity on package selection, and introduces an implicit package scoring and customer profiling recommendation method. The above indirect recommendation algorithms for electricity sales packages provide ideas for recommending these packages. Among them, accurate and efficient customer clustering is a prerequisite for improving the accuracy of electricity sales package recommendations. However, the aforementioned clustering methods all require the number of clusters to be pre-set, which results in lower clustering efficiency and accuracy. Reference [23] proposes a multi-attribute group decision-making method for electricity sales packages based on two-stage density clustering (TSDC) and minimum adjustment distance consensus. This method does not require pre-setting the number of clusters, making it a promising approach. Although these indirect recommendation methods consider customers' electricity costs, behavior characteristics, and preferences, they only consider the scenario where customers are familiar with all attributes of the packages, which is often unrealistic. Additionally, when customers face decision-making risks in multi-attribute group decision-making under uncertainty, they often exhibit some risk preferences. Therefore, integrating their risk preferences into multi-attribute decision-making is a meaningful task. Furthermore, in the problem of recommending electricity sales packages, the phenomenon of mixed multiple attribute values is common and cannot be ignored. For example, when customers evaluate electricity retail packages, they are often influenced by multiple factors such as electricity price, service, and electricity usage characteristics. Among these, electricity price can be represented by numerical values or estimations, while service is often described using fuzzy language. In such multi-attribute decision-making situations with uncertainty, reference [24] proposed a dynamic multi-attribute decision-making method under the three-parameter gray number information based on prospect theory, and reference [25] proposed a hybrid gray target decision-making method based on the \"convergence-divergence\" concept. In the problem of recommending electricity sales packages, there is not only uncertainty but also different sources of uncertainty in quantitative and qualitative attributes.