Research on Configuration Price Database Recommendation System Based on User Collaborative Filtering Algorithm

In order to meet the needs of auto consumers for personalised and featured auto configurations, and also to improve the accuracy and effect of the auto parts recommendation model, this paper proposes a user-based collaborative filtering algorithm for configuration price database recommendation system. Firstly, we constructed the ${{}^{\prime\prime}}5+15^{\prime\prime}$ configuration-price-aware database fields, and secondly, based on the traditional collaborative filtering algorithm, we not only introduced the attenuation feature function of interest and time, but also introduced the parameters of consumer attributes such as gender, age, and work field; then, we built a comprehensive recommendation system which contains consumer attributes and attenuation features; finally, the integrated recommendation system is applied in practice, and the results show that the algorithm achieves good results in practical application, with an accuracy rate as high as 82.3%. Therefore, this paper provides a new model for the recommendation system of the automobile industry, and the prediction results of this model basically meet the needs of the industry and help promote the high-quality development of the automobile industry.