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Method for recommending scientific and technological resources based on domain feature and latent semantic analysis

A technology of domain features and recommendation methods, applied in the field of scientific and technological resource data processing, can solve problems such as combining algorithms, no domain and implicit semantic analysis, no recommendation model, etc., to improve interpretability, avoid cold start problems, and improve time complexity. high degree of effect

Inactive Publication Date: 2016-04-20
GUANGDONG SCI & TECH INFRASTRUCTURE CENT
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Problems solved by technology

However, for scientific and technological resources, there is currently no relevant recommendation model, and there is no algorithm that combines domain and latent semantic analysis.

Method used

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  • Method for recommending scientific and technological resources based on domain feature and latent semantic analysis

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Embodiment 1

[0031] A method for recommending scientific and technological resources based on domain characteristics and latent semantic analysis, comprising the following steps:

[0032] S1: Through domain clustering of scientific and technological resources, users and resources are effectively classified according to domains to form user domains and resource domains, and preprocessed to obtain user-user domain datasets, user domain-resource domain datasets, resources domain - resource dataset;

[0033] S2: Use the user domain and resource domain to build a recommendation model based on domain features and implicit semantic analysis;

[0034] S3: Obtain the user's demand information, and recommend the required resources for the user through the established recommendation model based on domain characteristics and implicit semantic analysis.

[0035] Further, the specific process of the step S1 is as follows:

[0036] Define active users, user fields, resource fields, hot data, attention ...

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Abstract

The invention provides a method for recommending scientific and technological resources based on a domain feature and latent semantic analysis. According to the method, a user domain and a resource domain are introduced, a classification which is suitable for describing a user and resources is found to help a latent semantic analysis arithmetic to form a meaningful subject classification, the phenomenon that the classification significance cannot be explained by the latent semantic analysis arithmetic is improved, an efficient and quick slope-one arithmetic is utilized first to calculate three large data sets to form a P, L, and Q matrix, the problem that the time complexity of the latent semantic analysis arithmetic is high is solved, the method is applied to the recommendation of the scientific and technological resources with wide cross fields, huge data volume and relatively fixed user groups, and the problem of cold star existing in the latent semantic analysis arithmetic is solved effectively.

Description

technical field [0001] The invention relates to the field of data processing of scientific and technological resources, and more specifically, to a method for recommending scientific and technological resources based on domain characteristics and implicit semantic analysis. Background technique [0002] In the knowledge era, scientific and technological resources are becoming more and more important in the development of the national economy, and the degree of sharing and utilization in scientific and technological activities has also been highly valued by relevant departments and enterprises, and the multi-dimensional big data characteristics of resources have been highlighted. In order to improve the scientific and technological innovation environment and provide strong basic conditions for the majority of scientific and technological workers and scientific and technological activities, this patent researches the recommendation method of scientific and technological resourc...

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/9535G06F40/30
Inventor 罗亮林珠方少亮徐迪威李海威黄皓璇
Owner GUANGDONG SCI & TECH INFRASTRUCTURE CENT
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