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A market-based approach to recommender systems

Authors:

Nicholas R. JenningsAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 23, Issue 3

Pages 227 - 266

https://doi.org/10.1145/1080343.1080344

Published: 01 July 2005 Publication History

Abstract

Recommender systems have been widely advocated as a way of coping with the problem of information overload for knowledge workers. Given this, multiple recommendation methods have been developed. However, it has been shown that no one technique is best for all users in all situations. Thus we believe that effective recommender systems should incorporate a wide variety of such techniques and that some form of overarching framework should be put in place to coordinate the various recommendations so that only the best of them (from whatever source) are presented to the user. To this end, we show that a marketplace, in which the various recommendation methods compete to offer their recommendations to the user, can be used in this role. Specifically, this article presents the principled design of such a marketplace (including the auction protocol, the reward mechanism, and the bidding strategies of the individual recommendation agents) and evaluates the market's capability to effectively coordinate multiple methods. Through analysis and simulation, we show that our market is capable of shortlisting recommendations in decreasing order of user perceived quality and of correlating the individual agent's internal quality rating to the user's perceived quality.

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 23, Issue 3

July 2005

135 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/1080343

Issue’s Table of Contents

Copyright © 2005 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2005

Published in TOIS Volume 23, Issue 3

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Cited By

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https://doi.org/10.3390/electronics11213466
Xiao WZhao HPan HSong YZheng VYang Q(2021)Social explorative attention based recommendation for content distribution platformsData Mining and Knowledge Discovery10.1007/s10618-020-00729-135:2(533-567)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s10618-020-00729-1
Neto JMorais AGonçalves RCoelho A(2021)Multi-Agent-Based Recommender Systems: A Literature ReviewProceedings of Sixth International Congress on Information and Communication Technology10.1007/978-981-16-2377-6_51(543-555)Online publication date: 24-Sep-2021
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Xiao WZhao HPan HSong YZheng VYang QTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Beyond PersonalizationProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330965(235-245)Online publication date: 25-Jul-2019
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Afolabi AToivanen PHaataja KMykkänen J(2018)Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommender SystemsIntelligent Systems10.4018/978-1-5225-5643-5.ch098(2206-2226)Online publication date: 2018
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Afolabi AToivanen P(2018)Recommender Systems in HealthcareHandbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics10.4018/978-1-5225-5460-8.ch014(323-346)Online publication date: 11-May-2018
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Wang HDu ROlsen T(2018)Feedback mechanisms and consumer satisfaction, trust and repurchase intention in online retailInformation Systems Management10.1080/10580530.2018.147730135:3(201-219)Online publication date: 29-May-2018
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