Sentence recommendation method and device based on associated interest points

Abstract

The present invention proposes a sentence recommendation method and device based on associated points of interest, wherein the method includes: obtaining user input sentences; extracting keywords in the input sentences, and searching for matching keywords in the preset interest point association network Current point of interest; determine multiple related points of interest matching the current point of interest according to the preset point of interest association network, and filter out multiple related points of interest according to the preset screening strategy; expand the point of interest according to the current point of interest and the extension The point of interest retrieves the first reply sentence and the second reply sentence from the preset corpus and provides them to the user. Therefore, the extended interest point is guided to the extended interest point through the association between different interest points, and then the reply sentence containing the extended interest point is provided to the user, and then the user interest point set is expanded based on the user feedback, so that the user interest profile can be expanded more efficiently , to increase the diversity of topics in man-machine dialogue and improve user experience.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a sentence recommendation method and device based on associated interest points. Background technique [0002] At present, in the human-machine dialogue scenario, chatbots usually use the user's voice information as input, and automatically decide the semantic content of the reply by analyzing the voice information, combining the previous dialogue history of the two parties, user interest information, etc. [0003] However, the initial user interest information is usually specified by the user, or comes from a small number of brief interactions with the user, usually the granularity of the information is not fine enough (for example, only at the level of specific fields: entertainment and sports), and the coverage is not enough, and it usually relies on the active guidance of the user Find a new topic, and then extract user interest information based on th...

AI Technical Summary

Problems solved by technology

[0003] However, the initial user interest information is usually specified by the user, or comes from a small number of brief interactions with the user, usually the granularity of the information is not fine enough (for example, only at the level of specific fields: entertainment and sports), and the coverage is not enough, and it usually relies on the active guidance of the user Find a new topic, and then extract user interest information based on the content of the user's conversation. Because it is completely dependent on the user to start the topic, it is easy to fall into a relatively closed topic collection, and the accumulation process is relatively inefficient, resulting in a relatively low coverage of user interest.

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