research-article

Recommendation Technologies for Configurable Products

Authors:

Andreas Falkner,

Alexander Felfernig,

Albert HaagAuthors Info & Claims

AI Magazine, Volume 32, Issue 3

Pages 99 - 108

https://doi.org/10.1609/aimag.v32i3.2369

Published: 01 September 2011 Publication History

Abstract

State‐of‐the‐art recommender systems support users in the selection of items from a predefined assortment (for example, movies, books, and songs). In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are represented in the form of a configuration knowledge base that describes the properties of allowed instances. Although the knowledge representation used is different compared to nonconfigurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this article we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research.

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Cited By

Burgstaller TGarber DLe VFelfernig A(2024)Optimization Space Learning: A Lightweight, Noniterative Technique for Compiler AutotuningProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3672588(36-46)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3672588
Lubos SBoratto LGena CMarras MGermanakos PPopescus E(2024)Improving Recommender Systems with Large Language ModelsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664919(40-44)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664919
Lubos SLe VFelfernig ATran T(2023)Analysis Operations for Constraint-based Recommender SystemsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608819(709-714)Online publication date: 14-Sep-2023
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Published In

cover image AI Magazine

AI Magazine Volume 32, Issue 3

Fall 2011

126 pages

ISSN:0738-4602

EISSN:2371-9621

DOI:10.1002/aaai.v32.3

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© 2011 The Authors. AI Magazine published by John Wiley & Sons Ltd on behalf of Association for the Advancement of Artificial Intelligence.

Publisher

John Wiley & Sons, Inc.

United States

American Association for Artificial Intelligence

United States

Publication History

Published: 01 September 2011

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Cited By

Burgstaller TGarber DLe VFelfernig A(2024)Optimization Space Learning: A Lightweight, Noniterative Technique for Compiler AutotuningProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3672588(36-46)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3672588
Lubos SBoratto LGena CMarras MGermanakos PPopescus E(2024)Improving Recommender Systems with Large Language ModelsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664919(40-44)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664919
Lubos SLe VFelfernig ATran T(2023)Analysis Operations for Constraint-based Recommender SystemsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608819(709-714)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608819
Lubos SFelfernig ALe VTran TBenavides DZamudio JGarber D(2023)Analysis Operations On The RunProceedings of the 27th ACM International Systems and Software Product Line Conference - Volume A10.1145/3579027.3608982(111-116)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1145/3579027.3608982
Smith JJayne LBurke R(2022)Recommender Systems and Algorithmic HateProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3551480(592-597)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3551480
Falkner AFriedrich GHaselböck ASchenner GSchreiner H(2016)Twenty‐Five Years of Successful Application of Constraint Technologies at SiemensAI Magazine10.1609/aimag.v37i4.268837:4(67-80)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1609/aimag.v37i4.2688

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