[1]刘昊东,王 诚.基于热门度修正因子和置信度的协同过滤算法[J].计算机技术与发展,2023,33(03):127-132.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 019]
　LIU Hao-dong,WANG Cheng.Collaborative Filtering Algorithm Based on Popularity Correction Factor and Confidence[J].,2023,33(03):127-132.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 019]
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基于热门度修正因子和置信度的协同过滤算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年03期
页码:
127-132
栏目:
人工智能
出版日期:
2023-03-10

文章信息/Info

Title:
Collaborative Filtering Algorithm Based on Popularity Correction Factor and Confidence
文章编号:
1673-629X(2023)03-0127-06
作者:
刘昊东; 王 诚
南京邮电大学 通信与信息工程学院,江苏 南京 210003
Author(s):
LIU Hao-dong; WANG Cheng
School of Telecommunications & Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
协同过滤算法; 数据稀疏; 热门度修正因子; 余弦相似度; 置信度
Keywords:
collaborative filtering algorithm; data sparsity; popularity correction factor; cosine similarity; confidence
分类号:
TP309
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 03. 019
摘要:
协同过滤算法作为推荐系统的核心算法,其思想是运用用户的历史数据去挖掘用户的兴趣爱好。 由于热门项目在传统协同过滤算法的计算过程中会被过分考虑,不能更好地反映用户的真实需求,并且该算法在收集用户评分信息时还存在数据稀疏性的问题,提出了一种结合热门度修正因子和置信度的协同过滤算法。 在修正余弦相似度计算公式中引入 Jaccard 函数来缓解评分矩阵的稀疏性,为了抑制热门项目对实际推荐效果的影响,将热门度修正因子引入到皮尔逊相似度计算公式中,最终相似度计算公式是通过上述改进的公式按照权重进行融合所生成。 在公开数据集上验证了引入因子的相似度计算公式以及最终改进相似度计算公式的有效性,从结果中可以清晰地看出其平均绝对误差 MAE( MeanAbsolute Error)有所降低。
Abstract:
As the core algorithm of the recommendation system,the collaborative filtering algorithm uses the user’s historical data to minethe user’s interests. Because popular items are over - considered in the calculation process of the traditional collaborative filteringalgorithm,which cannot better reflect the real needs of users,and the algorithm also has the problem of data sparsity when collecting userrating information,a collaborative filtering algorithm combining popularity correction factor and confidence is proposed. The Jaccardfunction is introduced into the revised cosine similarity calculation formula to alleviate the sparsity of the scoring matrix. In order tosuppress the influence of popular items on the actual recommendation effect,the popularity correction factor is introduced into the Pearsonsimilarity calculation formula. The final similarity calculation formula is generated by fusing the above improved formula according to theweight. We verify the validity of the similarity calculation formula of the introduced factor and the final improved similarity calculationformula on the public data set. It can be clearly seen from the results that the mean absolute error ( MAE) has been reduced.

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更新日期/Last Update: 2023-03-10