Coating Matching Recommendation Based on Improved Fuzzy Comprehensive Evaluation and Collaborative Filtering Algorithm

Abstract

Coating matching design is one of the important parts of ship coating process design. The selection of coating matching is influenced by various factors such as marine corrosive environment, anti-corrosion period and working conditions. There are also differences in the coating performance requirements for different ship types and different coating parts. At present, the design of coating matching in shipyards depends on the experience of technologist, which is not conducive to the scientific management of ship painting process and the macro control of ship construction cost. Therefore, this paper proposes a hybrid algorithm of fuzzy comprehensive evaluation and collaborative filtering based on user label improvement (IFCE-CF). User interest labels and hierarchical analysis are used to calculate the combined weights, and collaborative filtering algorithm based on matrix decomposition is used to achieve personalized recommendations for coating matching. Historical coating process data of a shipyard between 2010 and 2020 were selected to verify the recommendation ability of the method in the paper. The results show that using the coating matching intelligent recommendation algorithm proposed in this paper, Root Mean Square Error < 1.02 and Mean Absolute Error < 0.79, the prediction accuracy is significantly better than other research methods, which proves the effectiveness of the method.