Text sorting method, sorting device, server and computer readable storage medium

Abstract

The embodiment of the invention discloses a text sorting method and device based on artificial intelligence, a server and a computer readable storage medium. The method includes: generating the labelvalue corresponding to the sample recall document by utilizing the sorting position of the sample recall document in the sorted sample recall document set; predicting the sorting value of the recalleddocuments in the recalled document set according to the label value; obtaining a prediction sorting value of the recalled document; therefore, a reliable sorting sequence can be provided for each search; meanwhile, the sorting problem is converted into a document sorting model with high credibility from a binary classification mode of only clicking and not clicking by a user; and meanwhile, the tag value does not need to be manually labeled, so that the accuracy of the recommendation information can be improved according to the sorted recalled document set while the sorting efficiency of therecalled text is improved.

Description

technical field [0001] The present invention relates to the technical field of communication, in particular to a text sorting method, a sorting device, a server and a computer-readable storage medium. Background technique [0002] With the development of Internet technology, the amount of multimedia information is also increasing. In order to improve user experience, the multimedia information application usually recommends relevant multimedia information to the user according to the multimedia information currently viewed by the user. [0003] For example, the video application recalls relevant video content according to the search term entered by the user, and then recommends the sorted video content to the user. [0004] In the prior art, the method of estimating the click probability of video content at each location is: count the average click probability of video content at each location as the denominator, and then divide the predicted probability or score by the den...

AI Technical Summary

Problems solved by technology

[0004] In the prior art, the method of estimating the click probability of video content at each location is: count the average click probability of video content at each location as the denominator, and then divide the predicted probability or score by the denominator to obtain the final score , according to the sorting of the final scores, so as to recommend video content to users. However, this method has the coarsest granularity and the recommendation effect is not accurate enough.

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