Logistics recommendation method based on clustering and cosine similarity

Abstract

The invention discloses a logistics recommendation method based on clustering and cosine similarity. Firstly, the AP clustering method, the SDbw clustering method and the K-means clustering method are used to calculate the best K value for using the K-means clustering method of the cargo data set and the truck data set. According to the best K value, the cargo data set and the truck data set are subjected to clustering, and the naive Bayesian classifier is used to train two classifiers based on the results obtained from clustering of the cargo data set and the truck data set. The classifiers obtained by training of the truck data set and the cargo data set are used for classification, and then the cosine distances between normalized truck information and all elements of the same type of truck information in the truck data set are calculated, and finally the goods are recommended according to the cosine distances from large to small. The method effectively improves the real-time response speed of the recommendation method.

Description

technical field [0001] The invention belongs to the technical field of clustering methods and recommendation methods, and particularly relates to a logistics recommendation method based on clustering and cosine similarity. Background technique [0002] The logistics recommendation method plays an important role and significance in improving the transportation efficiency of goods in the logistics field. Traditional logistics only provide simple displacement, while modern logistics provides value-added services. Manual selection of goods or trucks can no longer meet the needs of the logistics field. In recent years, according to the needs of different recommendation systems, researchers have proposed corresponding personalized recommendation schemes, such as content-based recommendation, collaborative filtering, association rules, utility recommendation, combined recommendation and so on. [0003] The existing research foundations of Zhu Quanyin et al. include: Zhu Quanyin, Pa...

AI Technical Summary

Problems solved by technology

[0013] When the traditional method is used for logistics recommendation, it is necessary to search the nearest neighbors of the target user on the entire data set. With the increasing scale of the e-commerce system, the number of users and the number of items increase sharply. The nearest neighbor of is very time-consuming, and it is increasingly difficult to meet the real-time requirements of the recommendation system

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