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Market-based Recommendation: Agents that Compete for Consumer Attention

Market-based Recommendation: Agents that Compete for Consumer Attention
Market-based Recommendation: Agents that Compete for Consumer Attention
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 o®erings. 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.
Recommender Systems, Search Ads, Market-Based Control, Adaptive Agents
420-448
Bohte, S. M.
778642dc-81fe-474d-9a2d-b7c8cfdbf2a1
Gerding, E. H.
d9e92ee5-1a8c-4467-a689-8363e7743362
La Poutre, J. A.
275c5cb4-c376-4b65-b4d9-7574fb5532ab
Bohte, S. M.
778642dc-81fe-474d-9a2d-b7c8cfdbf2a1
Gerding, E. H.
d9e92ee5-1a8c-4467-a689-8363e7743362
La Poutre, J. A.
275c5cb4-c376-4b65-b4d9-7574fb5532ab

Bohte, S. M., Gerding, E. H. and La Poutre, J. A. (2004) Market-based Recommendation: Agents that Compete for Consumer Attention. ACM Transactions on Internet Technology, 4 (4), 420-448.

Record type: Article

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 o®erings. 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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Published date: November 2004
Keywords: Recommender Systems, Search Ads, Market-Based Control, Adaptive Agents
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 263434
URI: https://eprints.soton.ac.uk/id/eprint/263434
PURE UUID: 1f8f677e-cd45-465a-9415-02ee41135561
ORCID for E. H. Gerding: ORCID iD orcid.org/0000-0001-7200-552X

Catalogue record

Date deposited: 14 Feb 2007
Last modified: 07 Aug 2019 00:41

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