文献类型：期刊文献

英文题名：A New Online Education Personalized Recommendation Algorithm

作者：Pang, Zhaojun[1];Wu, Wenbin[2];Fan, Xinxin[3];Liu, Zhixin[4]

机构：[1]Xian Fanyi Univ, Sch Educ, Xian 710105, Peoples R China;[2]Xian Univ, Xian 710065, Peoples R China;[3]Univ Elect Sci & Technol China, Chengdu 610054, Peoples R China;[4]Shaoxing Univ, Sch Life Sci, Shaoxing 312000, Peoples R China

年份：2022

卷号：10

外文期刊名：JOURNAL OF ENGINEERING RESEARCH

收录：SCI-EXPANDED(收录号：WOS:000922229800004)、、WOS

语种：英文

外文关键词：Online Education; Recommendation algorithm; User score; Item attribute

外文摘要：For online education platforms, a personalized recommendation system is crucial, and the collaborative filtering algorithm is the primary recommendation algorithm used. This study took the recommendation of crowdfunding platforms as a sample, and enhanced the collaborative filtering algorithm based on the user score and project attribute features of the crowdfunding platform, intending to resolve the cold start issue brought on by the platform's reliance on a single data source. The study concludes with experimental proof of the paper's suggested better method. This approach can alleviate the cold start issue to some degree. The prediction accuracy has been much enhanced in comparison with the conventionally advised method. The method can also adapt to user tastes over time, learning what they like and what they don't. It also has an excellent real-time suggestion impact. The performance verification of the algorithm in this research is also conducted using data from a live crowdfunding site, lending credence to the study's claim of greater practicality.