As part of the cultural industries, performing arts has been playing an important role in enriching people’s spiritual life, leveling culture and education, creating jobs, and even making economic benefits. Hence, a significant methodology is required to tackle the complicated concepts of transformational leadership (TL) and social factors in an arts industry context. This article aims to observe the direct impacts of TL on organizational commitment (OC). Furthermore, it examined the indirect effects of TL on OC and intention to stay (ITS) via person–job fit (PJF), social capital (SC), and psychological capital (PC). According to the results of this research, TL was discovered to have a positive impact on OC and PJF while having no significant effect on ITS. Furthermore, PJF had a significant impact on SC. Moreover, SC significantly influenced PC. In addition, PC was discovered to be in a significant correlation with OC while having no significant association with ITS. Finally, OC was also in a significant relationship with ITS.

In order to solve the problem of insufficient accuracy of consumer recommendation systems, the study proposes to design a new network inference algorithm with bias based on the traditional network inference algorithm. Then the performance of this algorithm is verified by means of comparison experiments with NBI, SNBI and HNBI algorithms. The results show that the new network inference algorithm has an accuracy rate of 24.5%, which is better than the traditional network inference algorithm. In terms of the constituted system performance test, the recommendation hit rate of the novel network inference algorithm increases by 13.97%, which is better than NBI, SNBI, and HNBI. the experimental results indicate that the novel network inference algorithm with bias can improve the performance of the consumer recommendation system, which provides a new idea for the performance improvement of the consumer recommendation system.