UI Design and Optimization Method for Museum Display Based on User Behavior Recommendation

Abstract

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.