[1]丛洪杰,龚安,李华昱,等.基于用户兴趣和项目分类的协同过滤推荐算法[J].计算机技术与发展,2018,28(11):85-88.[doi:10.3969/ j. issn.1673-629X.2018.11.019]
　CONG Hong-jie,GONG An,LI Hua-yu,et al.A Collaborative Filtering Recommendation Algorithm Based on User Interest and Item Classification[J].,2018,28(11):85-88.[doi:10.3969/ j. issn.1673-629X.2018.11.019]
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基于用户兴趣和项目分类的协同过滤推荐算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年11期
页码:
85-88
栏目:
智能、算法、系统工程
出版日期:
2018-11-10

文章信息/Info

Title:
A Collaborative Filtering Recommendation Algorithm Based on User Interest and Item Classification
文章编号:
1673-629X(2018)11-0085-04
作者:
丛洪杰1; 龚安1; 李华昱1; 帅训波2
1. 中国石油大学(华东) 计算机与通信工程学院,山东 青岛 266580; 2. 中国石油勘探开发研究院 计算机应用技术研究所,北京 100083
Author(s):
CONG Hong-jie1; GONG An1; LI Hua-yu1; SHUAI Xun-bo2
1.School of Computer &Communication Engineering,China University of Petroleum (East China),Qingdao 266580,China; 2.Institute of Computer Application Technology,China Petroleum Exploration and Development Research Institute,Beijing 100083,China
关键词:
协同过滤; 修正余弦相似性; 用户兴趣分布; 项目分类
Keywords:
collaborative filtering; modified cosine similarity; user interest distribution; item classification
分类号:
TP391
DOI:
10.3969/ j. issn.1673-629X.2018.11.019
文献标志码:
A
摘要:
随着用户产生的评分数据的稀疏性越来越高,传统的协同过滤算法在计算用户相似性和项目相似性的过程中暴露出弊端,导致推荐质量急剧下降。 针对用户评分矩阵数据稀疏性高、推荐精度低等问题,首先采用用户兴趣分布对评分矩阵预测填充,以降低数据的稀疏性,然后在寻找最近邻居的过程中,提出基于项目分类的修正余弦相似性的度量方法,利用用户评分在项目类别内的偏离程度来改进修正余弦相似性,以寻找更加准确的 K 近邻。 在 MovieLens 1m 数据集上进行了实验,结果表明,在用户评分数据稀疏性较高的情况下,该方法可以有效地改善传统的余弦相似性,修正余弦相似性度量方法所存在的问题,显著提高了推荐质量。
Abstract:
As the sparsity of the score data generated by users becomes higher and higher,the traditional collaborative filtering algorithm exposes defects in the calculation of user similarity and project similarity,leading to a sharp decline in recommendation quality. Aiming at the problems of high sparsity and low recommendation accuracy of user score matrix data,we firstly use the user interest distribution to predict and fill the score matrix,so as to reduce the sparsity of data. Secondly,we propose a measure method of modified cosine similarity based on item attribute classification in the process of finding nearest neighbors,and use the degree of deviation of user rating in the item attribute category for a more accurate K-nearest neighbor. The experiment is carried out in the MovieLens 1m datasets,which shows that in the case of high sparse of user score data,the proposed method can effectively improve the traditional cosine similarity,correct the problems existing in the cosine similarity measurement method,and remarkably improve the recommended quality.

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更新日期/Last Update: 2018-11-10