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Market-based recommendation: Agents that compete for consumer attention

Authors:

Sander M. Bohte,

Enrico Gerding,

Han La PoutréAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 4, Issue 4

Pages 420 - 448

https://doi.org/10.1145/1031114.1031118

Published: 01 November 2004 Publication History

Abstract

The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the "consumer attention space," or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of "consumer attention space" for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings.

For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding-strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains.

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Cited By

Oliveira LSchneider DDe Souza JShen W(2019)Mobile Device Detection Through WiFi Probe Request AnalysisIEEE Access10.1109/ACCESS.2019.29254067(98579-98588)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2925406
Fortino GGuerrieri ARosaci DSarné G(2018)Integrating Traditional Stores and e-Commerce into a Multi-tiered Recommender System Architecture Supported by IoTInternet and Distributed Computing Systems10.1007/978-3-319-97795-9_5(50-62)Online publication date: 24-Jul-2018
https://doi.org/10.1007/978-3-319-97795-9_5
Palopoli LRosaci DSarné G(2016)A distributed and multi-tiered software architecture for assessing e-Commerce recommendationsConcurrency and Computation: Practice & Experience10.1002/cpe.379828:18(4507-4531)Online publication date: 25-Dec-2016
https://dl.acm.org/doi/10.1002/cpe.3798
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Market-based recommendation: Agents that compete for consumer attention

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 4, Issue 4

November 2004

108 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/1031114

Issue’s Table of Contents

Copyright © 2004 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]

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Publication History

Published: 01 November 2004

Published in TOIT Volume 4, Issue 4

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Cited By

Oliveira LSchneider DDe Souza JShen W(2019)Mobile Device Detection Through WiFi Probe Request AnalysisIEEE Access10.1109/ACCESS.2019.29254067(98579-98588)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2925406
Fortino GGuerrieri ARosaci DSarné G(2018)Integrating Traditional Stores and e-Commerce into a Multi-tiered Recommender System Architecture Supported by IoTInternet and Distributed Computing Systems10.1007/978-3-319-97795-9_5(50-62)Online publication date: 24-Jul-2018
https://doi.org/10.1007/978-3-319-97795-9_5
Palopoli LRosaci DSarné G(2016)A distributed and multi-tiered software architecture for assessing e-Commerce recommendationsConcurrency and Computation: Practice & Experience10.1002/cpe.379828:18(4507-4531)Online publication date: 25-Dec-2016
https://dl.acm.org/doi/10.1002/cpe.3798
Palopoli LRosaci DSarnè G(2013)Introducing specialization in e-commerce recommender systemsConcurrent Engineering10.1177/1063293X1349391521:3(187-196)Online publication date: 8-Jul-2013
https://doi.org/10.1177/1063293X13493915
Palopoli LRosaci DSarné G(2013)A Multi-tiered Recommender System Architecture for Supporting E-CommerceIntelligent Distributed Computing VI10.1007/978-3-642-32524-3_10(71-81)Online publication date: 2013
https://doi.org/10.1007/978-3-642-32524-3_10
Marca D(2013)E-Business and Social Networks: Tapping Dynamic Niche Markets Using Language-Action and Artificial IntelligenceSoftware and Data Technologies10.1007/978-3-642-29578-2_1(3-23)Online publication date: 2013
https://doi.org/10.1007/978-3-642-29578-2_1
Cattelan RKirovski D(2012)Towards improving the online shopping experienceWeb Intelligence and Agent Systems10.5555/2589968.258997410:2(209-231)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.5555/2589968.2589974
Rosaci DSarné G(2012)A multi-agent recommender system for supporting device adaptivity in e-CommerceJournal of Intelligent Information Systems10.1007/s10844-011-0160-938:2(393-418)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1007/s10844-011-0160-9
Jiang YShang JKemerer CLiu Y(2011)Optimizing E-tailer Profits and Customer SavingsMarketing Science10.1287/mksc.1100.063130:4(737-752)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1287/mksc.1100.0631
Bothos EChristidis KApostolou DMentzas GCantador IBrusilovsky PKuflik T(2011)Information market based recommender systems fusionProceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems10.1145/2039320.2039321(1-8)Online publication date: 27-Oct-2011
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