Amazon.com recommendation - Item-to-item collaborative filtering

被引：3099

作者：

Linden, G

Smith, B

York, J

机构：

来源：

IEEE INTERNET COMPUTING | 2003年 / 7卷 / 01期

关键词：

D O I：

10.1109/MIC.2003.1167344

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Recommendation algorithms are best known for their use on e-commerce Web sites. It provides an effective form of targeted marketing by creating a personalized shopping experience for each customer. Amazon.com uses them to personalize the online store for each customer. Most of these algorithms start by finding a set of customers whose purchased and rated items overlap the user's purchased and rated items.

引用

页码：76 / 80

页数：5

共 10 条

[1]

[Anonymous], 1998, TECHNICAL REPORT WS

[2] Fab: Content-based, collaborative recommendation [J].

Balabanovic, M ;

Shoham, Y .

COMMUNICATIONS OF THE ACM, 1997, 40 (03) :66-72

[3]

Bradley P. S., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining, P9

[4]

BRESSE J, 1998, P 14 C UNC ART INT, P43

[5] Eigentaste: A constant time collaborative filtering algorithm [J].

Goldberg, K ;

Roeder, T ;

Gupta, D ;

Perkins, C .

INFORMATION RETRIEVAL, 2001, 4 (02) :133-151

[6]

Linden G.D., 2001, U.S. Patent, Patent No. [6,266,649, 6266649]

[7]

Resnick Paul, 1994, P ACM C COMP SUPP CO, P175, DOI DOI 10.1145/192844.192905

[8]

Sarwar B, 2001, P 10 INT C WORLD WID, P285

[9]

Sarwar B., 2000, Analysis of recommendation algorithms for e-commerce, Proceedings of the Second ACM Conference on E-commerce (EC), P158

[10]

SCHAFER JB, 2001, DATA MIN KNOWL DISC, V5, P115, DOI DOI 10.1023/A:1009804230409

← 1 →