Abstract
Aiming at the problems of low recommendation accuracy and low user preference in traditional methods, a personalised recommendation method of online educational resources on social media platform is proposed. Firstly, the crawler technology is used to obtain the online educational resources data, and the resource data features are extracted. Then, the similarity of data features is calculated through cosine similarity, and the feature data with high similarity is fused to complete the feature preprocessing of educational resources data. Finally, the user's demand for resource data and preference degree are determined through the user interest model, so as to construct the online educational resources personalised recommendation model, and take the educational resources data and user preference degree as the input data to complete the educational resources personalised recommendation. The experimental results show that the proposed method has high accuracy and user preference.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
