Analysis of recommendation algorithms for e-commerce

Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1369 Scopus citations

Abstract

Recommender systems apply statistical and knowledge dis- covery techniques to the problem of making product recom- mendations during a live customer interaction and they are achieving widespread success in E-Commerce nowadays. In this paper, we investigate several techniques for analyzing large-scale purchase and preference data for the purpose of producing useful recommendations to customers. In par- ticular, we apply a collection of algorithms such as tradi- tional data mining, nearest-neighbor collaborative filtering, and dimensionality reduction on two different data sets. The first data set was derived from the web-purchasing transac- tion of a large E-commerce company whereas the second data set was collected from MovieLens movie recommenda- tion site. For the experimental purpose, we divide the rec- ommendation generation process into three sub processes{ representation of input data, neighborhood formation, and recommendation generation. We devise different techniques for different sub processes and apply their combinations on our data sets to compare for recommendation quality and performance.

Original languageEnglish (US)
Title of host publicationEC 2000 - Proceedings of the 2nd ACM Conference on Electronic Commerce
PublisherAssociation for Computing Machinery, Inc
Pages158-167
Number of pages10
ISBN (Electronic)9781581132724
DOIs
StatePublished - Oct 17 2000
Event2nd ACM Conference on Electronic Commerce, EC 2000 - Minneapolis, United States
Duration: Oct 17 2000Oct 20 2000

Publication series

NameEC 2000 - Proceedings of the 2nd ACM Conference on Electronic Commerce

Conference

Conference2nd ACM Conference on Electronic Commerce, EC 2000
Country/TerritoryUnited States
CityMinneapolis
Period10/17/0010/20/00

Bibliographical note

Funding Information:
Funding for this research was provided in part by the National Science Foundation under grants IIS 9613960, IIS 9734442, and IIS 9978717 with additional funding by Net Perceptions Inc. This work was also supported by NSF CCR-9972519, EIA-9986042, ACI-9982274 by Army Research O ce contract DA/DAAG55-98-1-0441, by the DOE ASCI program and by Army High Performance Computing Re- search Center contract number DAAH04-95-C-0008. Ac- cess to computing facilities was provided by AHPCRC, Min- nesota Supercomputer Institute. Our special thanks to Na- dav Cassuto and Deb Campbell of Fingerhut Inc. for the E-Commerce data set. We also thank anonymous reviewers for their valuable comments.

Publisher Copyright:
© 2000 ACM. All rights reserved.

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