RETRACTED: Research on Home Product Design and Intelligent Algorithm Recommendation considering Ergonomics (Retracted Article)

Under the modern design concept, consider ergonomics to design home products. With the progress of civilization and technology, the improvement of life quality in the process of urbanization, and the increasing abundance of home life and home products, people's requirements for living environment and environmental products are continuously improving. In order to further meet the necessities of life and solve the reasons such as limited living space at home, people are no longer satisfied with purchasing household products in large quantities but are more suitable for household needs. According to the user's requirements for ergonomic home product design, a criterion layer is established, and the weight of the criterion layer is calculated to obtain its corresponding weight value. It can be obtained that consumers think that safety is the most important, followed by ease of use, functionality, and aesthetics. In the second criterion level, the order of importance is stable operation, safe use of materials, invisible circuit, strong practicability, massage function, safety guardrail, convenient installation, easy cleaning, intelligent operation, home style, structural strength, easy to move, natural materials, air purification, easy disassembly, suitable size, simple shape, convenient function, timely after-sales, soft color tone, noise reduction, simple decoration, single color matching, and comfortable function. The addition of the nearest neighbors improves the accuracy of the CFCNN-CL algorithm and the REPREDICT PCC algorithm in terms of smart algorithm recommendations for home products considering ergonomics. But compared between the two, the CFCNN-CL algorithm has better performance and better accuracy than the REPREDICT PCC algorithm. In terms of the influence of data sparseness, UCF-Jaccard has a smaller MAE value than other methods in general and is less susceptible to the influence of sparse data, and the MAE value does not change much. Among the group filtering methods, the RRP-UICL method has better prediction accuracy than the commonly used group filtering methods.