Precision Management of Agricultural Products Wholesale Platform Based on Big Data Technology

Abstract

Abstract In today’s big data-driven information technology development, the use of algorithms for platform management to achieve more accurate and efficient user services has become a trend. In this paper, from the two aspects of platform recommendation and route delivery of agricultural products wholesale, combining platform recommendation algorithms and delivery route optimization algorithms, the accurate management of the agricultural products wholesale platform is studied. Taking the dataset collected from the X agricultural products wholesale platform as the experimental sample, it can be seen by comparing the experimental results of this paper’s recommendation algorithm and the traditional collaborative filtering algorithm that this paper’s platform recommendation algorithm is more effective. Among them, the recommendation algorithm based on user similarity performs better than the recommendation algorithm based on user location in terms of accuracy, coverage, and recall, with optimal values of 36.42%, 50.84%, and 17.17%, respectively. Taking the route distribution study of 10 customers on the X agricultural products wholesale platform as an example, comparing the original planned distribution routes and the optimized routes, the total distance of the optimized routes is 84.97km, which is 21.29% less than that of the original planned routes under the condition of meeting the customer’s demand volume, distribution time and truck loading capacity, which verifies the effectiveness of the distribution route optimization algorithm. Effect validation shows that customer stickiness, relationship quality, and conversion cost are significantly correlated with platform recommendations and delivery route optimization.