[1]薛 亮,徐 慧,冯尊磊,等.一种改进的协同过滤的商品推荐方法[J].计算机技术与发展,2022,32(07):201-207.[doi:10. 3969 / j. issn. 1673-629X. 2022. 07. 035]
　XUE Liang,XU Hui,FENG Zun-Lei,et al.An Improved Co-filtered Goods Recommendation Method[J].,2022,32(07):201-207.[doi:10. 3969 / j. issn. 1673-629X. 2022. 07. 035]
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一种改进的协同过滤的商品推荐方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年07期
页码:
201-207
栏目:
应用前沿与综合
出版日期:
2022-07-10

文章信息/Info

Title:
An Improved Co-filtered Goods Recommendation Method
文章编号:
1673-629X(2022)07-0201-07
作者:
薛 亮1; 2; 徐 慧2; 冯尊磊3; 贾俊铖4
1. 苏州城市学院 计算机工程系,江苏 苏州 215104;
2. 苏州大学文正学院 计算机工程系,江苏 苏州 215104;
3. 浙江大学 计算机科学与技术学院,浙江 杭州 310027;
4. 苏州大学 计算机科学与技术学院,江苏 苏州 215006
Author(s):
XUE Liang1; 2 ; XU Hui2 ; FENG Zun-Lei3 ; JIA Jun-cheng4
1. Computer Engineering Department,Suzhou City University,Suzhou 215104,China;
2. Computer Engineering Department,Wenzheng College of Soochow University,Suzhou 215104,China;
3. School of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China;
4. School of Computer Science and Technology,Soochow University,Suzhou 215006,China
关键词:
电子商品; 推荐; 协同过滤; 皮尔逊相关系数; 相似度
Keywords:
electronic goods; recommendation; collaborative filtering; Pearson correlation coefficient; similarity
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 07. 035
摘要:
传统的电子商务平台无法实现对用户进行个性化优质商品的推荐,大量的商品信息无法被充分应用在帮助客户选择商品上。 针对上述问题,提出了改进的协同过滤算法( new Pearson collaborative filtering,NP-CF) 为用户进行个性化的电子商品推荐。 该算法弱化了活跃用户的对商品相似度的贡献程度并且利用标准差的计算降低电子商品本身质量对相似度的影响,将两者计算获得的系数与皮尔逊关系数相结合,从而计算出更加准确的用户相似度,再利用相似度值计算商品的推荐值并且通过加权评价公式对该值进行加权处理。 最后在真实的数据集 MovieLens 和人工数据集 Mobile-Data上对该算法进行实验测试,且与传统的基于用户信息的协同过滤算法( user collaborative filtering,User-CF) 进行比对,该算法(NP-CF) 整体上优化了推荐结果并且提高了推荐的准确率。
Abstract:
Traditional e-commerce platforms are unable to realize personalized high - quality goods recommendations for users, causing users to face a large amount of goods information,but the utilization rate of information is gradually decreasing.? ?In response to the above problems,we propose an improved collaborative filtering algorithm ( new Pearson collaborative filtering,NP-CF) to provide users with personalized electronic goods recommendations. The algorithm weakens the contribution of active users to goods similarity and uses the calculation of standard deviation to reduce the impact of the quality of electronic goods on similarity. The calculated coefficients of the two with the Pearson relationship number is combined to calculate a more accurate user similarity,then the similarity value is used to calculate the recommended value of the goods and weight the value through a weighted evaluation formula. Finally, the proposed algorithm is tested on the real data set called MovieLens and the artificial data set named Mobile-Data and compared with the traditional user collaborative filtering based on user information ( user-CF) . The proposed NP-CF optimizes the recommendation results as a whole and improves the accuracy.

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更新日期/Last Update: 2022-07-10