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Text recommendation method based on depth semantic discrimination

A technology of semantic discrimination and recommendation method, which is applied in the field of recommendation and can solve problems such as semantic correlation needs to be improved

Pending Publication Date: 2018-03-23
BEIJING UNIV OF TECH
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  • Claims
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AI Technical Summary

Problems solved by technology

[0005] In view of the deficiencies of the existing technology, the present invention provides a text recommendation method based on deep semantic discrimination, aiming to solve the problem that the semantic relevance of the existing recommendation method needs to be improved

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  • Text recommendation method based on depth semantic discrimination
  • Text recommendation method based on depth semantic discrimination
  • Text recommendation method based on depth semantic discrimination

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Embodiment Construction

[0051] The present invention provides a text recommendation method based on deep semantic discrimination. The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0052] figure 1 It is a flowchart of a preferred embodiment of a text recommendation method based on deep semantic discrimination in the present invention, as shown in the figure, and its implementation steps are:

[0053] S100. Construct a deep semantic grid model based on the brain-like "layered-divergent" thinking mode;

[0054] S101. The user inputs content of interest and sets a current scene state;

[0055] S102. Perform topic reasoning and topic situational semantic analysis on the text, construct a text topic tree, and perform topic semantic screening on the document topic tree according to the current user's situational state, thereby constructing a text topic tree that integrates situational semantic screening;

[0056] S103,...

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Abstract

The invention discloses a text recommendation method based on depth semantic discrimination. A text topic is automatically extracted according to a depth semantic grid model, scene semantics under different text backgrounds are inferred according to a topic scene semantics analysis method, a text topic tree which incorporates situational states is achieved, a user text interest portrait is constructed for each document based on a user's real-time situational state. At the query end, for the real-time fluctuations of the user's situational state, the text topic tree is screened for contextual semantics, the query content is modeled on the query interest topic, according to an activation diffusion method, second latent semantic reasoning is performed on the user's direct interest topic, theglobal activation value of the topic is calculated, and a user query interest portrait which incorporates the current context semantics is constructed. The documents are scored by a similarity calculation method, and a text recommendation list is generated according to the score.

Description

technical field [0001] The present invention relates to the technical field of recommendation, and relates to a text recommendation method based on deep semantic analysis, in particular to a deep semantic grid model based on a brain-like "layered-divergent" thinking mode and a recommendation method for text topic scene semantic analysis . Background technique [0002] The recommendation system was proposed in the 1990s. The early recommendation systems mainly focused on the formal similarity of the retrieval results, while ignoring the semantic correlation between the retrieval results and the query, resulting in a lot of noise in the recommendation results. In recent years, with the explosive growth of paperless data, the effectiveness of information retrieval has attracted widespread attention from researchers, and a variety of semantic-based information retrieval methods have been proposed. In terms of personalized semantic recommendation, it is mainly divided into two c...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/332G06F16/36G06F16/951Y02D10/00
Inventor 郐弘智陈建辉盛文瑾闫健卓
Owner BEIJING UNIV OF TECH
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