Recommendation of Resource Allocation Decision Based on Bipartite Graph Network Structure

In resource allocation decisions in business, fully understanding customers’ needs and preferences helps to maximise benefits. As a result, in the modern business environment, the design of customized recommendation systems has gained a lot of attention. To this end, the study designs a recommendation algorithm for resource allocation decision based on improved two-part graph network structure. In this algorithm, an improved K-means clustering algorithm is introduced to deeply mine potential information. The calculation of similarity between users is also optimised to assist the target user to find the real neighbouring users. The findings demonstrate that, in comparison to the other algorithms, the resource allocation recommendation algorithm based on improved bipartite graph suggested in the study has a greater hit rate. The hit rate of the suggested algorithm can reach 32.5% when the recommendation list length is 10, which is a 21.5% improvement over the collaborative filtering algorithm. The suggested algorithm’s popularity is only 39.1, which is 69.3 less than the collaborative filtering algorithm when the suggestion list length is 10. Furthermore, the suggested algorithm for resource allocation decision-making created by the research has a greater recommendation accuracy, more personalization, and diversity, as seen by the proposed algorithm’s mean Hamming distance of 0.976. Through an improved bipartite graph network, the algorithm can fully analyze the historical preference information of users, effectively capture the complex relationship between users and products, generate personalized recommendation lists, and improve user satisfaction and purchase conversion rates. It provides an effective recommendation role for resource allocation decisions in modern business and helps to create greater economic benefits.