基于改进的混合模式个性化选课推荐技术研究

针对高等学校学生选课系统中存在的缺乏个性化课程推荐、选课效率较低的问题,通过对个性化推荐技术的分析研究,提出了基于内容、项目及用户属性的改进混合模式算法,并将该算法应用到选课系统中,用MACE数据集对算法进行验证。结果表明,该算法解决了个性化推荐技术中的冷启动问题,相关指标有明显提高,实现了课程与新课程的个性化推荐,并减少了选课的盲目性。

Research on Improved Personalized Courses Recommendation Technology Based on Mixed Mode

Problems of lacking in individualized curriculum recommendations and inefficiency exist in current course selection systems of institutions of higher education.In allusion to these limitations,this paper presents a improved mixed model algorithm based on the content,project and user attribute-value through analysis and study of personalized recommendation technology.The proposed algorithm has been successfully applied to the elective system.Experimental results indicate that the proposed approach can solve cold-start technology in personalized recommendation algorithm,improve the related indicators significantly,achieve a personalized recommendation and new courses recommendation and reduce the blindness by the MACE data sets.