Personalized Recommendation System of College Students’ Employment Education Resources Based on Cloud Platform

Abstract

In view of the current use of the linkage university student employment service platform, the recommendation method under the “double innovation” education perspective is generally based on the stand-alone mode, with limited processing capabilities and scalability, resulting in low system recommendation accuracy and recall rates. In order to solve this problem, a personalized recommendation system for college students’ employment education resources based on a cloud platform is designed. Use the Elastic Search open source search engine to build indexes so that users can quickly locate the resources they need from the search results. Build a talent training decision-making system platform to enable students to choose high-quality module resources that they are interested in. Design a hypertext service platform to obtain recommended paths. Establish a keyword index platform to index educational resources based on keywords. Construct a vector space model, describe the keyword vector, and determine the similarity between the relevance of the courseware and the category. Analyze queries and documents, remove stop words, extract stems, and vectorize documents. The document content is represented by the feature weight set, and a personalized recommendation process is designed. It can be seen from the experimental results that the highest recommendation accuracy rate of the system is 99%, and the highest recall rate is 98%, which has an efficient recommendation effect.