Feature Association Algorithm of Similarities of Multidimensional Automobile Consumer Tags

This paper proposes a feature association algorithm for similarities of multidimensional consumer tags for automobiles to improve the accuracy of recommendation systems in the automobile industry and optimize user experience. The algorithm constructs a similarity matrix for automobile user tags, considering various interests and preferences of automobile users, to comprehensively capture the similarities between users. The Jaccard similarity matrix is used as the basic calculation method, which effectively measures the ratio of intersections and unions between different automobile user tags., thereby quantifying the similarity between automobile users. Through experiments with a large amount of automobile user data., the algorithm in this paper performs well in improving recommendation accuracy., especially in handling multidimensional automobile user preferences. Additionally., the algorithm can adapt flexibly to the dynamic changes in automobile user preferences., maintaining the timeliness and relevance of the recommendation system. This research provides a new perspective and practical approach for the development of user recommendation systems in the automobile industry and is expected to promote innovation and improvement in personalized services for automobile consumers in the future.