[1]陈鑫宇,杨冬黎,鲁金秋,等.基于学习者模型的文本学习资源推荐算法研究[J].计算机技术与发展,2020,30(06):77-81.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 015]
　CHEN Xin-yu,YANG Dong-li,LU Jin-qiu,et al.Research on Text-learning Resource Recommendation Algorithm Based on Learner Model[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(06):77-81.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 015]
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基于学习者模型的文本学习资源推荐算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年06期
页码:
77-81
栏目:
智能、算法、系统工程
出版日期:
2020-06-10

文章信息/Info

Title:
Research on Text-learning Resource Recommendation Algorithm Based on Learner Model
文章编号:
1673-629X(2020)06-0077-05
作者:
陈鑫宇; 杨冬黎; 鲁金秋; 衣存慧; 左富成; 张丽伟
东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318
Author(s):
CHEN Xin-yu; YANG Dong-li; LU Jin-qiu; YI Cun-hui; ZUO Fu-cheng; ZHANG Li-wei
School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China
关键词:
文本推荐; 学习资源; 模型; 算法; 学习者
Keywords:
text recommendation; learning resources; model; algorithm; learners
分类号:
TP312
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 06. 015
摘要:
解决学习者在网站中获取文本学习资源的准确性,避免学习者浪费大量的时间和精力等问题,提出并构建了“三位一体冶的基于学习者模型推荐系统。 首先,该系统通过分析用户行为,把用户分为普通用户和目标用户两大类。 然后针对每一类用户使用不同的推荐算法。通过在传统向量空间模型表示法的基础上引入语义相关度,使文本向量模型和学习者兴趣向量模型进行了更新,从而更好地根据学习者的学习兴趣来推荐中文文本学习资源,使推送文本学习资源变得有据可依。 其次,通过对这两个模型进行余弦相似度对比,更好地实现中文文本学习资源的个性化推荐。 最后,通过在家教服务系统上进行实验仿真,验证了所提算法对推荐准确度的提高。 实验结果表明该算法是有效的。
Abstract:
In order to solve the problem that learners acquire the accuracy of text learning resources on the website and avoid wasting a lot of time and energy, we propose and construct a “trinity’ learner model-based recommendation system. First of all,the system divides users into ordinary users and target users by analyzing their behaviors. Then different recommendation algorithms are used for each type of users. Through the introduction of semantic relevance on the basis of traditional vector space model representation, the text vector model and learner interest vector model are updated,so as to better recommend Chinese-text learning resources according to learners’ learning interests,and make the text-learning resources become available. Secondly,by comparing the cosine similarity between the two models,the personalized recommendation of Chinese text resour-ces is realized. Finally,the experiment simulation on the tutoring-servicesystem shows the proposed algorithm,which is effective,can improve the recommendation accuracy.

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[1]聂黎生.基于行为分析的学习资源个性化推荐[J].计算机技术与发展,2020,30(07):34.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 008]
　NIE Li-sheng.Personalized Recommendation of Learning Resources Based on Behavior Analysis[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(06):34.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 008]
[2]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.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 006]
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更新日期/Last Update: 2020-06-10