基于内容过滤PageRank的Top-k学习资源匹配推荐  

Top-k Learning Resource Matching Recommendation Based on Content Filtering PageRank

作　　者：梁婷婷[1] 李春青[2] 李海生[2] 

机构地区：[1]广西师范大学计算机科学与信息工程学院,广西桂林541004 [2]广西民族师范学院数学与计算机科学系,广西崇左532200

出　　处：《计算机工程》2017年第2期220-226,共7页Computer Engineering

基　　金：广西高校科学技术研究项目(YB2014417)

摘　　要：针对在线教育支持技术中关于文本处理的多义词和同义词问题,提出基于内容过滤PageRank语义相似替换的Top-k学习资源推荐算法。基于内容的向量空间滤波建立学习资源过滤推荐模型,该模型采用资源间匹配方式以取代语义相似性,从而避免多义词或同义词的漏检问题。基于谷歌PageRank算法结合前述资源间匹配模型构建考虑资源间关系连接的权重矩阵,取代传统PageRank算法网页间的超链接方式,进行资源类型划分,得到特征的马尔可夫收敛矩阵,并利用Top-k算法实现推荐结果细化。实验结果表明,在公共学习资源数据集中,所提算法对计算时间的覆盖率是可行的。Aiming at the problem of the polysemous words and synonyms of text processing in the online education support technology,a Top-k learning resource recommendation algorithm based on content filtering PageRank is proposed.A learning resource filtering recommendation model is constructed based on content vector space filtering.The model pays attention to resource matching mode to replace the semantic similarity,so as to avoid missing detection of polysemous words or synonyms.Google PageRank algorithm is combined with the aforementioned resource matching model to construct weight matrix considering the relationship between resources.This is used to replace the hyperlink mode between Web pages of the traditional PageRank algorithm for resource type dividing.The Markov convergence matrix of characteristics is constructed,and the Top-k algorithm is used to refine the recommended results.Experimental results show that the proposed algorithm is feasible for the computation time cover rate in the public learning resource dataset.

关 键 词：内容过滤 PAGERANK算法 Top-k排序 马尔可夫收敛矩阵 资源匹配 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]