Conversion rate evaluation method and device

Abstract

The embodiment of the invention provides a conversion rate evaluation method and a device, and the method comprises the steps: obtaining feature data according to a commodity attribute table and a user attribute table; inputting the obtained feature data into a preset click Wide & Deep model, outputting the click probability of the user to the commodity link, inputting the obtained feature data into a sharing Wide & Deep model, and outputting the sharing probability of the user to the commodity link; according to the click probability and the sharing probability, after score fusion is carriedout, obtaining the conversion probability of the commodity by the user; wherein the click or share Wi-Fi & Deep model is obtained by training according to the sample commodity attribute table and thesample user attribute table of the determined click or share result. According to the method, the click probability and the sharing probability of the user on the commodity can be accurately obtained,and the determined conversion probability is more accurate through fusion of the two fine ranking scores and the recall score.

Description

technical field [0001] The invention relates to the field of data analysis, in particular to a conversion rate evaluation method and device. Background technique [0002] With the rapid development of the Internet, the information that people can access has changed from relatively scarce to relatively excessive. How to obtain the most relevant content for users from the massive amount of information has become the core issue for Internet companies to meet user needs. The problem of information overload can be solved by launching a recommendation system on the website. The recommendation system has greatly improved the user satisfaction and service revenue of these Internet companies. The mainstream recommendation systems can be divided into three categories of methods: (1) content-based recommendation; (2) collaborative filtering; (3) hybrid methods. In the online deployment of the actual website, the recommendation system often needs to cooperate with the client log system...

AI Technical Summary

Problems solved by technology

[0005] The current evaluation method has the following limitations: (1) Only one user behavior (such as click behavior) is considered, but there are many kinds of behaviors in the actual production environment, and the existing methods cannot model multiple user behaviors

(2) There is room for improvement in the accuracy of existing methods

(3) The system efficiency optimization degree of the existing method is low, the degree of parallelism is insufficient, and it cannot meet the real-time requirements of online services, and the feasibility of deployment is not high

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