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A method of scholar recommendation and cooperative prediction based on representation learning and competition theory

A technology of scholars and theories, applied in the field of scholar recommendation and cooperative prediction based on representation learning and competition theory, can solve problems such as poor results

Active Publication Date: 2019-01-04
DALIAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0006] The present invention provides a scholar recommendation and cooperation prediction method based on representation learning and competition theory, which solves the problem of poor effect caused by traditional static recommendation and single-source cooperation prediction, realizes dynamic cooperation recommendation and cooperation prediction, and ensures the accuracy of results accuracy

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  • A method of scholar recommendation and cooperative prediction based on representation learning and competition theory
  • A method of scholar recommendation and cooperative prediction based on representation learning and competition theory
  • A method of scholar recommendation and cooperative prediction based on representation learning and competition theory

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Embodiment Construction

[0042] The technical solutions of the present invention will be further described below in conjunction with specific embodiments and accompanying drawings.

[0043] Such as figure 1 As shown, the embodiment of the present invention discloses a scholar recommendation and cooperation prediction method based on representation learning and competition theory, including the following steps:

[0044] Step 1. Obtain effective data from the Microsoft dataset and preprocess it, and divide it into training set and test set;

[0045] Preprocessing: Obtain scholar groups based on scholar information, filter scholars with low cooperation numbers, obtain effective scholar groups, and establish four factor files of effective scholar groups; taking the title factor file as an example, each piece of information in the file is a python dictionary Type storage, the name of the scholar is the key, and the title information of the scholar’s ​​paper is the value (when the scholar participates in p...

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Abstract

The invention provides a scholar recommendation and cooperative prediction method based on representation learning and competition theory, belonging to the field of computer software. With the huge academic network, the recommendation needs are met, through the presentation learning technology, the paper published by the author and the network relationship among scholars are analyzed, with the help of competition theory, the time conflict is solved in order to obtain the recommendation results and collaborative forecasting. The method obtains valid data from Microsoft dataset and preprocessesthem, constructs a dynamic model for calculating the similarity of scholars' personality based on the content of the paper, constructs a collaborative network-based model for computing the similarityof academic environments, and constructs the processing model of competition theory, the model is trained with the preprocessed data set, according to the personality similarity obtained from the training, a preliminary recommendation list is generated to weaken the source and target scholars who are too similar by using the environmental similarity, and the time conflict is eliminated by using the processing model of competition theory, so as to achieve effective scholar recommendation and the next collaborative object prediction.

Description

technical field [0001] The invention belongs to the field of computer software, and relates to a scholar recommendation and cooperation prediction method based on representation learning and competition theory. Background technique [0002] Interdisciplinary research across multiple fields has grown rapidly over the past few decades, and scientific collaboration among scholars has become increasingly important and necessary. However, finding the most valuable collaborators from large academic data is often a great challenge. The purpose of representation learning is to assign a vector in a certain linear space to each node in the network, which is what we often call an N-dimensional vector. The relationship between vectors and vectors retains the relationship between the corresponding nodes in the original network. structural relationship. Word2vec was released by Google in 2013. It integrates the CBOW and Skip-Gram models and provides an efficient implementation for compu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/33G06F17/27
CPCG06F40/247G06F40/289
Inventor 孔祥杰闻琳燕夏锋张晨薇刘晓钟
Owner DALIAN UNIV OF TECH
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