辽宁工程技术大学学报（自然科学版）2017，Vol.36Issue(9)：976-982,7.DOI:10.11956/j.issn.1008-0562.2017.09.015
基于近邻模型与概率矩阵分解的高校选课推荐算法
A recommendation algorithm for elective courses in university based on nearest neighbor model and probability matrix factorization
摘要
Abstract
In order to study the problem of personalized recommendation for student elective courses online in college educational administration information management system,an improved algorithm based on nearest neighbor model and probability matrix factorization was proposed.Firstly this paper identified the similar students(elective courses) by measuring the similar relationship among students(elective courses).Then the nearest neighbor of similarity for students(elective courses) was applied to the collaborative filtering algorithm that based on probability matrix factorization.Finally,the Top-k recommendation rank was given on the basis of predictive scores and limiting conditions.The experimental results of prototype system show that the algorithm can be suitable for university elective courses recommendation application and solve the data sparse problem effectively.关键词
协同过滤/概率矩阵分解/近邻模型/高校选课/个性化推荐Key words
collaborative filtering/probability matrix factorization/nearest neighbor model/university elective courses/personalized recommendation分类
信息技术与安全科学引用本文复制引用
陈万志,张爽,王德建,王星..基于近邻模型与概率矩阵分解的高校选课推荐算法[J].辽宁工程技术大学学报（自然科学版）,2017,36(9):976-982,7.基金项目
辽宁省自然科学基金(2015020098) （2015020098）
辽宁工程技术大学博士基金(20151147) （20151147）
