Sequencing system based on machine learning

Abstract

The invention provides a sequencing system based on machine learning. A data collection and data pre-processing module collects data needing to be sequenced and learned. A training set building module comprises an initial stage, wherein partial samples are picked randomly from large quantities of stored unmarked inquiry-text data and an initial training set is built according to relativity manual marks, and an iteration stage, wherein a sequencing leaning module is called, a sampling algorithm is carried out according to a built sequencing model, and relativity marking is carried out on a sample with most information selected from the unmarked data. The marked data are stored in a storage module; the sequencing learning module builds the sequencing model; the training set building module and the sequencing learning module operate in an interactive iteration mode; a prediction sequencing module calls the sequencing model, inquires what is input by a user, retrieves corresponding text predicted relativity and sequences according to the relativity. Sequencing accuracy of the retrieval results can be improved through the sequencing system, the retrieval results can be well shown and user requirements are met.

Description

technical field [0001] The present invention relates to the fields of machine learning and information retrieval, in particular to a sorting system based on machine learning. Background technique [0002] Today's society is an information society. With the development of modern science and technology, information has grown explosively. How to quickly and accurately find the information you need from the massive amount of information is the core problem that information retrieval technology needs to solve. Whether information retrieval can better meet the needs of users is directly related to whether the massive information is fully utilized, which is of great significance to economic and social development. [0003] As a core technical problem in the field of information retrieval, ranking has been widely used in information retrieval problems such as web search, recommendation, and online advertisement. Taking web search as an example, such as Google, Baidu, Bing, Yahoo, ...

AI Technical Summary

Problems solved by technology

However, the disadvantage is that due to ignoring the relationship between the ranking model and the training set, the selected training set is difficult to guarantee the performance of the ranking model, which leads to the low ranking accuracy of the ranking system and is difficult to meet the needs of users.

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