[1]李吉祺,黄 刚.提取关键字改进协同过滤算法的研究与应用[J].计算机技术与发展,2019,29(06):154-158.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 032]
　LI Ji-qi,HUANG Gang.Research and Application of Improved Collaborative Filtering Algorithm of Keyword Extraction[J].,2019,29(06):154-158.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 032]
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提取关键字改进协同过滤算法的研究与应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年06期
页码:
154-158
栏目:
应用开发研究
出版日期:
2019-06-10

文章信息/Info

Title:
Research and Application of Improved Collaborative Filtering Algorithm of Keyword Extraction
文章编号:
1673-629X(2019)06-0154-05
作者:
李吉祺; 黄 刚
南京邮电大学 计算机学院,江苏 南京 210000
Author(s):
LI Ji-qi; HUANG Gang
School of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210000,China
关键词:
推荐系统; 协同过滤; 稀疏矩阵; 词频与逆文本频率指数; 混合推荐
Keywords:
recommendation system; collaborative filtering; sparse matrix; TF-IDF; mixed recommendation
分类号:
TP301.6
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 06. 032
摘要:
协同过滤算法在遇到数据稀疏性问题时,其相似度计算过程会受到很大的影响,导致推荐结果不准确,影响推荐系统用户体验。 而影评网站的影评往往很好地概括了电影的特征,从影评网站的影评文字中可以使用关键字提取算法提取特征来进行电影间的相似性计算。 TF-IDF 是一种高效而常用的关键词提取技术,其通过特定文档中词的相对频率和整个文档语料库中该词的反比例进行比较,最终得出该篇文章的关键字。 利用文本信息提取关键字,进而通过文章的关键字词进行文章的相似度计算,可以有效地改进评价矩阵稀疏的问题。 通过爬取电影的评价文字来进行关键字提取,改进评分矩阵较稀疏的电影的相似度计算,可以弥补稀疏矩阵的缺陷。 实验结果表明,该算法有效提高了准确率、召回率和覆盖率,证明了算法的可行性。
Abstract:
When the collaborative filtering algorithm is influenced by data sparsity,its similarity calculation process will be greatly affected,resulting in inaccurate recommendation and affecting the user experience of the recommendation system. The movie reviews on movie review websites often summarize the characteristics of movie,where keyword extraction algorithm can be used to extract features to calculate the similarity between movies. The TF-IDF is an efficient and commonly used keyword extraction technique,which compares the relative frequency of words in a specific document with the inverse proportion of the words in the entire document,and finally derives the keywords of the article. Using text information to extract keywords and then calculating the similarity of articles through the keyword words of the article can effectively improve the sparse evaluation matrix. To make up for the defects of the sparse matrix,the keyword can be extracted by crawling the movie reviews of the movie. Experiment shows that the proposed algorithm,which is proved to be feasible,can effectively improve the accuracy,recall rate and coverage.

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更新日期/Last Update: 2019-06-10