Article

Designing recommender systems for e-commerce: an integration approach

Author:

Thomas TranAuthors Info & Claims

ICEC '06: Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet

Pages 512 - 518

https://doi.org/10.1145/1151454.1151530

Published: 13 August 2006 Publication History

Abstract

In electronic commerce applications, prospective buyers may be interested in receiving recommendations to assist with their purchasing decisions. Previous research has described two main models for automated recommender systems: collaborative filtering and knowledge-based approaches. In this paper, we present an architecture for designing a hybrid recommender system that combines these two approaches. We then discuss how such a recommender system can switch between the two methods, depending on the current support for providing good recommendations from the behavior of other users, required for the collaborative filtering option. We also comment on how the overall design is useful to support recommendations for a variety of product areas and present some directions for future work.

References

[1]

Personallogic recommender system: http://www.personallogic.com.

[2]

H. K. Bhargava, S. Sridhar, and C. Herrick. Beyond spreadsheets: Tools for building decision support systems. IEEE Computer, 32(3):31--39, 1997.

Digital Library

[3]

D. Billsus and M. J. Pazzani. Learning collaborative information filters. Papers from The AAAI Workshop on Recommender Systems WS-98-08, AAAI Press, Menlo Park, California, 1998.

[4]

R. Burke. Integrating knowledge-based and collaborative-filtering recommender systems. Papers from The AAAI Workshop on Artificial Intelligence for Electronic Commerce WS-99-01, AAAI Press, Menlo Park, California, 1999.

[5]

R. Burke, K. Hammond, and E. Cooper. Knowledge-based navigation of complex information spaces. In Proceedings of the 13th National Conference on Artificial Intelligence, pages 462--468, 1996.

Digital Library

[6]

D. C. DeRoure, W. Hall, S. Reich, A. Pikrakis, G. J. Hill, and M. Stairmand. Memoir an open framework for enhanced navigation of distributed information. Information Processing and Management, 37:53--74, 2001.

Digital Library

[7]

D. R. Greening. Collaborative filtering for web marketing efforts. Papers from The AAAI Workshop on Recommender Systems WS-98-08, AAAI Press, Menlo Park, California, 1998.

[8]

J. L. Herlocker, J. A. Konstan, and J. Riedl. Explaining collaborative filtering recommendations. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, pages 241--250, 2000.

Digital Library

[9]

J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Information Systems, 22(1):5--53, 2004.

Digital Library

[10]

J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3):77--87, 1997.

Digital Library

[11]

M. Montaner, B. Lopez, and J. L. Dela. A taxonomy of recommender agents on the internet. Artificial Intelligence Review, 19:285--330, 2003.

Digital Library

[12]

H. Nguyen and P. Haddawy. Diva: Applying decision theory to collaborative filtering. Papers from The AAAI Workshop on Artificial Intelligence for Electronic Commerce WS-99-01, AAAI Press, Menlo Park, California, 1999.

[13]

A. Popescul, L. H. Ungar, D. M. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (UAI-2001), pages 437--444, 2001.

Digital Library

[14]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, pages 175--186, 1994.

Digital Library

[15]

P. Resnick and H. R. Varian. Recommender systems. Communications of the ACM, 40(3):56--58, 1997.

Digital Library

[16]

U. Shardanand and P. Maes. Social information filtering: Algorithms for automating word of mouth. In Proceedings of Computer Human Interaction, pages 210--217, 1995.

Digital Library

[17]

L. Terveen and W. Hill. Beyond recommender systems: Helping people help each other. In HCI in the New Millennium, J. Carroll, Ed. Addison Wesley, 2001.

Cited By

Rawat STyagi USinghal S(2021)Recommender Systems in E-commerce and their Challenges2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N53548.2021.9725681(1598-1601)Online publication date: 17-Dec-2021
https://doi.org/10.1109/ICAC3N53548.2021.9725681
Alamdari PNavimipour NHosseinzadeh MSafaei ADarwesh A(2020)A Systematic Study on the Recommender Systems in the E-CommerceIEEE Access10.1109/ACCESS.2020.30028038(115694-115716)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3002803
Peiris KGallupe R(2018)Evaluating a Prototype of a Recommender‐Driven Online Learning SystemDecision Sciences Journal of Innovative Education10.1111/dsji.1216416:4(281-309)Online publication date: 15-Oct-2018
https://doi.org/10.1111/dsji.12164
Show More Cited By

Index Terms

Designing recommender systems for e-commerce: an integration approach
1. Applied computing
  1. Enterprise computing
    1. Business process management
  2. Operations research
    1. Decision analysis
2. Information systems
  1. Information systems applications
    1. Decision support systems

Recommendations

Recommender systems for product bundling

Recommender systems (RS) are a class of information filter applications whose main goal is to provide personalized recommendations, content, and services to users. Recommendation services may support a firm's marketing strategy and contribute to ...
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
Incorporating frequency, recency and profit in sequential pattern based recommender systems

Customers usually change their purchase interests in the short product life cycle of the e-commerce environment. Therefore, recent transaction patterns should have a greater effect on the customer preferences. From the seller's point of view, an e-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICEC '06: Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet

August 2006

624 pages

ISBN:1595933921

DOI:10.1145/1151454

Conference Chairs:
Mark S. Fox
University of Toronto, Canada
,
Bruce Spencer
National Research Council Canada

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 August 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Acceptance Rates

ICEC '06 Paper Acceptance Rate 53 of 112 submissions, 47%;

Overall Acceptance Rate 150 of 244 submissions, 61%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
1,118
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)1

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Rawat STyagi USinghal S(2021)Recommender Systems in E-commerce and their Challenges2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N53548.2021.9725681(1598-1601)Online publication date: 17-Dec-2021
https://doi.org/10.1109/ICAC3N53548.2021.9725681
Alamdari PNavimipour NHosseinzadeh MSafaei ADarwesh A(2020)A Systematic Study on the Recommender Systems in the E-CommerceIEEE Access10.1109/ACCESS.2020.30028038(115694-115716)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3002803
Peiris KGallupe R(2018)Evaluating a Prototype of a Recommender‐Driven Online Learning SystemDecision Sciences Journal of Innovative Education10.1111/dsji.1216416:4(281-309)Online publication date: 15-Oct-2018
https://doi.org/10.1111/dsji.12164
Amitha Peiris KGan I(2013)A recommender systems approach to optimising career pathways development planning for youth in emerging knowledge economies2013 International Conference on Advances in ICT for Emerging Regions (ICTer)10.1109/ICTer.2013.6761162(98-103)Online publication date: Dec-2013
https://doi.org/10.1109/ICTer.2013.6761162
Peiris KGallupe R(2012)A Conceptual Framework for Evolving, Recommender Online Learning SystemsDecision Sciences Journal of Innovative Education10.1111/j.1540-4609.2012.00347.x10:3(389-412)Online publication date: 4-Jul-2012
https://doi.org/10.1111/j.1540-4609.2012.00347.x
Abdullah NXu YGeva S(2010)A recommender system for infrequent purchased products based on user navigation and product review dataProceedings of the 2010 international conference on Web information systems engineering10.5555/2044492.2044495(13-26)Online publication date: 12-Dec-2010
https://dl.acm.org/doi/10.5555/2044492.2044495
Abdullah NXu YGeva SChen J(2010)Infrequent Purchased Product Recommendation Making Based on User Behaviour and Opinions in E-commerce SitesProceedings of the 2010 IEEE International Conference on Data Mining Workshops10.1109/ICDMW.2010.116(1084-1091)Online publication date: 13-Dec-2010
https://dl.acm.org/doi/10.1109/ICDMW.2010.116
Kazienko P(2008)Web-Based Recommender Systems and User Needs --the Comprehensive ViewProceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems10.5555/1565754.1565780(243-258)Online publication date: 30-Jun-2008
https://dl.acm.org/doi/10.5555/1565754.1565780
Jiao YCao G(2007)A Collaborative Tagging System for Personalized Recommendation in B2C Electronic Commerce2007 International Conference on Wireless Communications, Networking and Mobile Computing10.1109/WICOM.2007.892(3604-3607)Online publication date: Sep-2007
https://doi.org/10.1109/WICOM.2007.892

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents