User knowledge demand model establishing method based on Gaussian mixed model

A Gaussian mixture model and construction method technology, which is applied in the field of user knowledge demand model construction based on Gaussian mixture model, and can solve problems such as the inability to reflect the semantic information of knowledge content.

Active Publication Date: 2017-09-29
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

However, the existing model construction technology mainly uses keywords and their weights to construct the user's knowledge demand model, which cannot reflect the semantic information of the knowledge content. This

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  • User knowledge demand model establishing method based on Gaussian mixed model
  • User knowledge demand model establishing method based on Gaussian mixed model
  • User knowledge demand model establishing method based on Gaussian mixed model

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Embodiment Construction

[0046] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0047] The present invention proposes a method for constructing a user knowledge demand model based on a Gaussian mixture model, the schematic diagram is as follows figure 1 shown.

[0048] The demand model constructed by the present invention mainly considers the user's demand for functional vocabulary and knowledge items. The present invention can achieve: 1) Knowing the knowledge items browsed by the user to deduce the user's knowledge requirement for functional vocabulary. The functional vocabulary representing the user's knowledge needs is extracted from the knowledge items browsed by the user, and the user's knowledge demand model for the functional vocabulary is trained. The representation of functional vocabulary is in vector form, which reflects the semantic information of vocabulary. 2) Knowing the user's knowledge demand model for functional ...

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Abstract

The invention provides a method for establishing a user knowledge demand model by utilizing a Gaussian mixed model for the first time. Firstly, high-dimensional vectors of function words are generated by considering the semantic information of the function words based on a skip-gram model of knowledge base training word2vec, then the Gaussian mixed model is trained by utilizing selected knowledge corpus set, multiple Gaussian distributions are applied to describe the probability distributions of function word knowledge demands of a user, an EM method is applied to optimize parameters of the Gaussian mixed model; finally, the mapping relation between the words and entries is established, a knowledge entry demand model of the user is obtained, and knowledge entries, most possibly interested by the user, in a knowledge base are calculated on the basis and are pushed to the user. The established Gaussian mixed model can more closely fit the user knowledge demand model, and the knowledge push accuracy rate is improved.

Description

technical field [0001] The invention belongs to the field of knowledge services, in particular to a method for constructing a user knowledge demand model based on a Gaussian mixture model. Background technique [0002] With the dramatic improvement of computer computing power and the rapid expansion of the network in recent years, knowledge resources have become very abundant no matter in the open Internet or in the local area network within the scope of enterprises. With the continuous increase of knowledge, designers spend more and more time searching for knowledge in the knowledge management system, and it is even difficult to find the knowledge that meets the requirements, which is not conducive to the sharing and reuse of design knowledge, and also hinders designers from designing Improvements in efficiency and design. The way of actively pushing knowledge is in line with the status quo of most design companies. It can effectively meet the needs of personnel, realize k...

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Application Information

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IPC IPC(8): G06F17/27G06N5/00
CPCG06F40/253G06F40/284G06N5/00
Inventor 郝佳杨念王国新阎艳杨剑雄余俊贾良跃
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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