In view of the lack of rich display methods in the display design of museums, it is impossible to enhance the interest of visitors. This paper proposes a museum object recommendation method based on collaborative filtering, which simplifies the display design, improves the recommendation effect, and alleviates the scalability problem. Firstly, the algorithm of recommendation system combines the advantages of memory collaborative filtering and uses smoothing processing to improve the efficiency of recommendation and achieve the best consistency. Then, the cross-domain collaborative filtering rating matrix generation model is used to establish the correlation between multiple rating matrices by finding the shared hidden clustering rating matrix, which also improves the recommendation effect. Finally, the conclusion shows that we can use single user behavior data such as forgetting mechanism to recommend to users. SVD makes full use of the interaction data of various behaviors, and NMF algorithm makes full use of the data of various user behaviors, which can effectively solve the existing problems. The stochastic gradient descent is applied to the SVD algorithm to accelerate the convergence speed of the model, improve the performance of the model, and effectively improve the accuracy of score prediction.
In order to improve the digital transformation effect of enterprise accounting talents, this paper combines intelligent methods to carry out the digital transformation of enterprise accounting talents from the perspective of blockchain. Moreover, this paper studies the sliding window CS-SCHT algorithm in depth. Based on the theoretical derivation of the sliding window CS-SCHT based on the gray code kernel, the algorithm is implemented on the computer platform, and the test experiment of the operation time is carried out. In addition, this paper explores the application of the sliding window CS-SCHT algorithm in adaptive filtering. The experimental results show that the adaptive filter based on the CS-SCHT algorithm can obtain a higher signal-to-noise ratio than the sliding window DFT algorithm. Finally, this paper constructs an intelligent accounting digital information processing system. The research shows that the system proposed in this paper can play an important role in the digital transformation of enterprise accounting talents from the perspective of blockchain.
Radial distribution system is an important link connecting power supply and users, and its power supply reliability is directly related to users. Radial distribution network reconfiguration can transform the network structure by changing the switching state of the distribution network lines, and achieve the goals of reducing network operational losses, improving power quality, and power supply reliability while meeting various constraints such as radial operation, power supply and demand balance, capacity, and voltage. Radial distribution systems have the characteristics of multiple components and complex structures. How to quickly and accurately evaluate the health performance of radial distribution systems and find an optimal solution for network reconfiguration are important issues in distribution network analysis. The network health performance evaluation of radial distribution system is classical multiple attributes group decision making (MAGDM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the network health performance evaluation of radial distribution system. In this paper, we extend the classical grey relational analysis (GRA) method to the probabilistic hesitancy fuzzy MAGDM with unknown weight information. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are briefly introduced. Then, the definition of the score values is employed to compute the attribute weights based on the information entropy method. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MAGDM under PHFSs. Finally, a practical case study for network health performance evaluation of radial distribution system is designed to validate the proposed method and some comparative studies are also designed to verify the applicability.
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