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E-Commerce Recommendation Applications

Authors:

J. Ben Schafer,

Joseph A. Konstan,

John RiedlAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 5, Issue 1-2

Pages 115 - 153

https://doi.org/10.1023/A:1009804230409

Published: 01 January 2001 Publication History

Abstract

i>Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledgeeither hand-coded knowledge provided by experts or mined knowledge learned from the behavior of consumersto guide consumers through the often-overwhelming task of locating products they will like. In this article we present an explanation of how recommender systems are related to some traditional database analysis techniques. We examine how recommender systems help E-commerce sites increase sales and analyze the recommender systems at six market-leading sites. Based on these examples, we create a taxonomy of recommender systems, including the inputs required from the consumers, the additional knowledge required from the database, the ways the recommendations are presented to consumers, the technologies used to create the recommendations, and the level of personalization of the recommendations. We identify five commonly used E-commerce recommender application models, describe several open research problems in the field of recommender systems, and examine privacy implications of recommender systems technology.

References

[1]

Agrawal, R., Imielinski, T., and Swami, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD-93, pp. 207-216.

Digital Library

[2]

Avery, C., Resnick, P., and Zeckhauser, R. 1999. The market for evaluations. American Economic Review, 89(3):564-583.

[3]

Balabanovic, M. and Shoham, Y. 1997. Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3):66-72.

Digital Library

[4]

Basu, C., Hirsh, H., and Cohen, W. 1998. Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the 1998 National Conference on Artificial Intelligence (AAAI-98), pp. 714-720.

Digital Library

[5]

Breese, J., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 43-52.

Digital Library

[6]

Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J., and Riedl, J. 1999. Combining collaborative filtering with personal agents for better recommendations. In Proceedings of AAAI-99, AAAI Press. pp. 439-446.

Digital Library

[7]

Herlocker, J., Konstan, J. A., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of SIGIR'99, pp. 230-237.

Digital Library

[8]

Hill, W., Stead, L., Rosenstein, M., and Furnas, G. 1995. Recommending and evaluating choices in a virtual community of use. In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, pp. 194-201.

Digital Library

[9]

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

Digital Library

[10]

Mani, D. R., Drew, J., Betz, A., and Datta, P. 1999. Statistics and data mining techniques for lifetime value modeling. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 94-103.

Digital Library

[11]

Peppers, D. and Rogers, M. 1997. The One to One Future: Building Relationships One Customer at a Time. Bantam Doubleday Dell Publishing, New York, NY.

[12]

Pine II, B. J. 1993. Mass Customization. Boston: Harvard Business School Press.

[13]

Pine II, B. J. and Gilmore, J. H. 1999. The Experience Economy. Boston: Harvard Business School Press.

[14]

Pine II, B. J., Peppers, D., and Rogers, M. 1995. Do you want to keep your customers forever? Harvard Business School Review, 1995(2):103-114.

[15]

Reichheld, F. 1993. Loyalty-based management. Harvard Business School Review, 1993(2):64-73.

[16]

Reichheld, F. and Sasser, W. E. 1990. Zero defections: Quality comes to services. Harvard Business School Review, 1990(5):105-111.

[17]

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM CSCW'94 Conference on Computer-Supported Cooperative Work, pp. 175-186.

Digital Library

[18]

Salton, G. 1968. Automatic Information Organization and Retrieval, McGraw-Hill Book Company, New York, NY.

Digital Library

[19]

Sarwar, B., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B., and Riedl, J. 1998. Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In Proceedings of 1998 Conference on Computer Supported Collaborative Work.

Digital Library

[20]

Schafer, J. B., Konstan, J. A., and Riedl, J. 1999. Recommender Systems in E-Commerce. In ACM Conference on Electronic Commerce (EC-99), pp. 158-166.

Digital Library

[21]

Shardanand, U. and Maes, P. 1995. Social information filtering: Algorithms for automating "word of mouth." In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, pp. 210-217.

Digital Library

[22]

Shneiderman, B. 1997. Direct manipulation for comprehensible, predictable, and controllable user interfaces. In Proceedings of IUI97, 1997 International Conference on Intelligent User Interfaces, Orlando, FL, January 6-9, 1997, pp. 33-39.

Digital Library

[23]

Wolf, J., Aggarwal, C., Wu, K-L., and Yu, P. 1999. Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Diego, CA.

Digital Library

Cited By

Qian KJain S(2024)Digital Content CreationManagement Science10.1287/mnsc.2022.0365570:12(8668-8684)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1287/mnsc.2022.03655
De Biasio AJannach DNavarin N(2024)Model-based approaches to profit-aware recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123642249:PBOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123642
Gupta KAhmad AGhosal TEkbal A(2024)A BERT-based sequential deep neural architecture to identify contribution statements and extract phrases for triplets from scientific publicationsInternational Journal on Digital Libraries10.1007/s00799-023-00393-y25:4(1-28)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s00799-023-00393-y
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Information & Contributors

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Published In

cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 5, Issue 1-2

January-April 2001

149 pages

ISSN:1384-5810

Issue’s Table of Contents

Copyright © Copyright © 2001 Kluwer Academic Publishers.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2001

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Qian KJain S(2024)Digital Content CreationManagement Science10.1287/mnsc.2022.0365570:12(8668-8684)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1287/mnsc.2022.03655
De Biasio AJannach DNavarin N(2024)Model-based approaches to profit-aware recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123642249:PBOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123642
Gupta KAhmad AGhosal TEkbal A(2024)A BERT-based sequential deep neural architecture to identify contribution statements and extract phrases for triplets from scientific publicationsInternational Journal on Digital Libraries10.1007/s00799-023-00393-y25:4(1-28)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s00799-023-00393-y
Lakshmi TBhavani S(2024)Link prediction approach to recommender systemsComputing10.1007/s00607-023-01227-0106:7(2157-2183)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00607-023-01227-0
Betello FSiciliano FMishra PSilvestri F(2024)Investigating the Robustness of Sequential Recommender Systems Against Training Data PerturbationsAdvances in Information Retrieval10.1007/978-3-031-56060-6_14(205-220)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_14
Mashayekhi YKang BLijffijt JDe Bie T(2023)ReCon: Reducing Congestion in Job Recommendation using Optimal TransportProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608817(696-701)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608817
Wang YGao MRan XMa JZhang L(2023)An improved matrix factorization with local differential privacy based on piecewise mechanism for recommendation systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119457216:COnline publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119457
Walek BFajmon P(2023)A hybrid recommender system for an online store using a fuzzy expert systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118565212:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118565
Nasir MEzeife C(2023)A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product RecommendationSN Computer Science10.1007/s42979-023-02166-54:6Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1007/s42979-023-02166-5
Torkashvand AJameii SReza A(2023)Deep learning-based collaborative filtering recommender systems: a comprehensive and systematic reviewNeural Computing and Applications10.1007/s00521-023-08958-335:35(24783-24827)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00521-023-08958-3
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