Employee individualized-learning recommendation method based on learning map and collaborative filtering

Abstract

The invention discloses an employee individualized-learning recommendation method based on a learning map and collaborative filtering.The method includes the steps that resource features and employee attributes are respectively extracted according to the learning resource content of an online learning platform and the practical conditions of learners (enterprise employees), a mathematical model is built, a recommendation list is calculated according to similarity to generate the recommendation result, the feedback conditions of the learners are collected to be used for improving similarity calculation, and the recommendation process is optimized.The method has certain universality in recommending the content of semi-structured data, unstructured data and multimedia learning resources, the learning map of the employees and collaborative filtering are combined, the recommendation effect is corrected and optimized, the sparse scoring matrix and learning resource recommendation, namely, cold starting, of new employees can be effectively achieved, pushing of the learning content of the online learning platform is more user-friendly, the enterprise employees are effectively assisted in rapid growing, employee training and learning cost is saved, and employee learning efficiency is improved.

Description

technical field [0001] The invention relates to the field of network technology, in particular to an employee personalized learning recommendation method based on learning maps and collaborative filtering. Background technique [0002] Online learning (e-learning) has become one of the effective ways for enterprises to carry out employee training. At present, many enterprises have built online learning support systems. With the increasing abundance of online learning resources and the more complex and diverse learning needs of learners, employees need to spend more time and energy to look up and retrieve learning resources that meet their needs in the platform, and even cannot find one that meets their own interests and positions. On the other hand, it is difficult to discover the potential learning needs of employees. However, the way of pushing learning resources in the traditional online learning system has great defects. Therefore, some researchers have used the succes...

AI Technical Summary

Problems solved by technology

With the increasing abundance of online learning resources and the more complex and diverse learning needs of learners, employees need to spend more time and energy to look up and retrieve learning resources that meet their needs in the platform, and even cannot find one that meets their own interests and positions. On the other hand, it is difficult to discover the potential learning needs of employees

However, the way of pushing learning resources in the traditional online learning system has major defects. Therefore, some researchers have used the successful experience of the recommendation system in the field of e-commerce to apply methods such as collaborative filtering to the online learning system.

However, the problems of sparse scoring matrix and cold start in the collaborative filtering method still exist in the learning recommendation system.

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