Transaction enterprise recommendation method and device, equipment and medium

Abstract

The invention discloses a transaction enterprise recommendation method and device, equipment and a medium. The method comprises the following steps: acquiring transaction data of a buyer enterprise and a seller enterprise in a preset time period; according to the transaction data, obtaining transaction parameters of the seller enterprise and the purchaser enterprise, and obtaining a purchaser value score of the purchaser enterprise relative to the seller enterprise based on the transaction parameters; according to the transaction data, determining a purchase proportion of the purchaser enterprise for a target type of product in different seller enterprises; and on the basis of the purchaser value score and the purchase proportion, determining a transaction enterprise recommendation list for each seller enterprise from the purchaser enterprises. Therefore, the recommendation list is generated by comprehensively considering the transaction parameters, the purchase proportion and other information, the recommendation accuracy and effectiveness are enhanced, the transaction success rate is improved, and the experience of purchaser and seller enterprise users is improved.

Description

technical field [0001] The present invention relates to the field of data mining, in particular to a method, device, equipment and medium for recommending trading companies. Background technique [0002] At present, large-scale purchase and sale transactions between enterprises are usually carried out through proprietary B2B (Business to Business) e-commerce platforms. The purchase and sale behavior between enterprises is quite different from that of a single user. For example, for relatively mature and stable businesses Purchaser enterprise users have a certain degree of stability and continuity in their purchasing behavior, and the switching cost of switching suppliers for purchasing is often relatively high, that is, the cost savings in purchasing is not enough to offset the switching cost. In the existing technology, some e-commerce platforms, music sharing platforms, and movie sharing platforms generally capture user interests and make personalized recommendations by ob...

AI Technical Summary

Problems solved by technology

In the existing technology, some e-commerce platforms, music sharing platforms, and movie sharing platforms generally capture user interests and make personalized recommendations by obtaining data such as user information, user retrieval and browsing, user comments, user purchases, and user social interactions. These recommendation methods have a certain effect on the basis of discovering the interests of individual users, but the actual application on the B2B e-commerce platform will reduce the recommendation effect

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