Knowledge recommendation method and system based on improved position social contact and storage medium

Abstract

The invention discloses a knowledge recommendation method and system based on improved position social contact and a storage medium. Knowledge points needing to be accessed by a user in the next stepare predicted by utilizing existing access data of the user and other data information in a knowledge network; the prediction result is recommended to the user to serve as a learning recommendation list of the user, the current situation that a common commodity-oriented recommendation model and algorithm are not suitable for recommendation on a knowledge learning system can be solved, and the knowledge obtaining efficiency of the user on the knowledge system and the learning efficiency of the user are improved; meanwhile, the defect that at present, common recommendation system models and algorithms are mainly used for recommending commercial articles is overcome; according to the method, the path set of the knowledge points accessed by the user is constructed, knowledge recommendation inthe knowledge social network is realized through an improved position social recommendation algorithm, and the efficiency of learning and understanding certain knowledge by the user is improved. The method can be applied to the field of knowledge recommendation systems.

Description

technical field [0001] The present invention relates to the field of recommendation systems, in particular to a knowledge recommendation method, system and storage medium based on improved social interaction. Background technique [0002] In the era of information explosion, the problem people face is no longer the inability to obtain data from the Internet, but the need to face the massive amount of information on the Internet. How to find the real valuable information in massive data has also become a hot research direction in the information age. At present, the information retrieval methods commonly used by users are generally search engines, such as Google, Baidu, etc. Commonly used search engines have the advantages of a wide range of applications and simple information retrieval functions that satisfy most users. However, when users have specific needs or users have individualized and diverse characteristics, search engines often cannot capture the complexity of user...

AI Technical Summary

Problems solved by technology

Commonly used search engines have the advantages of a wide range of applications and simple information retrieval functions that satisfy most users. However, when users have specific needs or users have individualized and diverse characteristics, search engines often cannot capture the complexity of users. features for personalized recommendations

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