Knowledge document recommendation method based on user historical behavior features

Abstract

The invention provides a knowledge document recommendation method based on user historical behavior features. According to the knowledge document recommendation method, the word frequency of each word in an article is calculated, the words and the word frequencies are used as items and support degrees respectively, and the article which is most relevant to the article which is uploaded by a user is excavated by means of the FP-Tree method. The knowledge document recommendation method comprises the steps that a knowledge base word library is extracted through word segmentation of articles which the user has read and are stored in a knowledge base; word tables in the user word library are scanned and optimized, the support degrees of the FP-Tree method is replaced by TF word frequencies so as to establish a FP tree, and a frequent item set containing user reading features is excavated; the most relevant articles are determined finally, the most relevant articles are ranked according to the importance degrees, and the ranked most relevant articles are recommended to the user. According to the knowledge document recommendation method based on the user historical behavior features, the words in the articles are used as excavation features, modeling is conducted on the historical reading behavior of each user, dependence on the reading behaviors of other users is avoided, and therefore the problems that a great number of valuable articles in an enterprise knowledge base are not read by people and the users cannot find the articles containing relevant knowledge at the same time are solved.

Description

technical field [0001] The invention relates to the technical field of enterprise knowledge base and intelligent recommendation, in particular to a knowledge document recommendation method based on user historical behavior characteristics. Background technique [0002] Enterprise knowledge bases have been set up in some large IT companies to store basic enterprise information: public relations information, annual reports, publications and general introduction of the enterprise, etc.; enterprise organizational structure information: addresses, agents, branches, service centers Information about products and services: technical expertise, service characteristics, etc.; basic process information; information about patents, trademarks, copyrights, use of other enterprise technologies and methodologies; customer information, etc. And many employees find it difficult to find the knowledge they need in the company. Often employees don't know where this information is stored, and th...

AI Technical Summary

Problems solved by technology

The disadvantage of this approach is that it depends on the user group, also known as "cold start", that is, when a system is just started and there are only a few users, the system cannot build an effective model

And if an article has not been read by users for a long time, it is difficult to use this model to recommend it to users no matter how high the knowledge value of this article is.

[0004] This solution urgently needs to solve the problem that no one reads the large amount of knowledge accumulated in the enterprise, and new employees cannot find the relevant knowledge.

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