[1]覃忠台,张明军.基于协同过滤算法的学习资源推荐模型研究[J].计算机技术与发展,2021,31(09):31-35.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 006]
　QIN Zhong-tai,ZHANG Ming-jun.Research on Learning Resource Recommendation Model Based on Collaborative Filtering Algorithm[J].,2021,31(09):31-35.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 006]
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基于协同过滤算法的学习资源推荐模型研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年09期
页码:
31-35
栏目:
大数据分析与挖掘
出版日期:
2021-09-10

文章信息/Info

Title:
Research on Learning Resource Recommendation Model Based on Collaborative Filtering Algorithm
文章编号:
1673-629X(2021)09-0031-05
作者:
覃忠台; 张明军
广州大学华软软件学院,广东 广州 510990
Author(s):
QIN Zhong-tai; ZHANG Ming-jun
Software Engineering Institute of Guangzhou,Guangzhou 510990,China
关键词:
协同过滤; 学习资源; 推荐模型; 在线学习; 行为信息
Keywords:
collaborative filtering; learning resources; recommendation model; online learning; behavior information
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 09. 006
摘要:
传统的推荐算法由于存在数据稀疏性和冷启动问题,导致在线学习平台在资源推荐上不能满足学习用户的个性化需求。 为此,构建一个基于协同过滤算法的学习资源推荐模型,在推荐过程中融入学习用户的属性特征信息,进行学习资源个性化推荐。 首先,在给出学习资源推荐模型的基础上分别构建了学习用户模型和资源模型;其次,在构建学习用户-学习资源评分矩阵的基础上采用基于修正的余弦相似度的改进算法结合学习用户的行为信息进行相似度计算和预测评分;最后,将学习用户模型和学习资源模型的特征信息融入推荐过程并实现学习资源的个性化推荐。 通过对模型测试和实验的 MAE 值比对分析,基于协同过滤算法的学习资源推荐模型在推荐精度和个性化方面均优于传统的推荐算法模型。
Abstract:
Due to the problems of data sparsity and cold start in the traditional recommendation algorithm,the online learning platform cannot meet the personalized needs of learning users in resource recommendation. Therefore, we construct a learning resource recommendation model based on collaborative filtering algorithm,integrate the attribute feature information of learning users into the recommendation process,and carry out personalized recommendation of learning resources. Firstly,based on the proposed learning resource recommendation model,the learning user model and the learning resource model are constructed respectively. Secondly,on the basis of constructing the learning user learning resource scoring matrix, the improved algorithm based on modified cosine similarity is used to calculate and predict the score combining with the behavior information of the learning user. Finally,the feature information of learning user model and learning resource model is integrated into the recommendation process, and personalized recommendation of learning resources is realized. Through the comparison and analysis of MAE value between model test and experiment,the learning resource recommendation model based on collaborative filtering algorithm is better than the traditional recommendation algorithm model in recommendation accuracy and personalization.

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更新日期/Last Update: 2021-09-10