Evaluating recommender systems in feature model configuration.

Mathias Uta; Alexander Felfernig; Viet Man Le; Andrei Popescu; Thi Ngoc Trang Tran; Denis Helic

doi:10.1145/3461001.3471144

Evaluating recommender systems in feature model configuration.

Mathias Uta, Alexander Felfernig, Viet Man Le, Andrei Popescu, Thi Ngoc Trang Tran, Denis Helic

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Configurators can be evaluated in various ways such as efficiency and completeness of solution search, optimality of the proposed solutions, usability of configurator user interfaces, and configuration consistency. Due to the increasing size and complexity of feature models, the integration of recommendation algorithms with feature model configurators becomes relevant. In this paper, we show how the output of a recommender system can be evaluated within the scope of feature model configuration scenarios. Overall, we argue that the discussed ways of measuring recommendation quality help developers to gain a broader view on evaluation techniques in constraint-based recommendation domains.

Original language	English
Title of host publication	Proceedings of the 25th ACM International Systems and Software Product Line Conference, SPLC 2021
Editors	Mohammad Mousavi, Pierre-Yves Schobbens, Hugo Araujo, Ina Schaefer, Maurice H. ter Beek, Xavier Devroey, Jose Miguel Rojas, Monica Pinto, Leopoldo Teixeira, Thorsten Berger, Johannes Noppen, Iris Reinhartz-Berger, Paul Temple, Ferruccio Damiani, Justyna Petke
Place of Publication	United States
Publisher	Association of Computing Machinery
Pages	58-63
Number of pages	6
DOIs	https://doi.org/10.1145/3461001.3471144
Publication status	Published - 6 Sept 2021
Event	25th ACM International Systems and Software Product Line Conference - Duration: 6 Sept 2021 → 11 Sept 2021

Conference

Conference	25th ACM International Systems and Software Product Line Conference
Abbreviated title	SPLC 2021
Period	06/09/2021 → 11/09/2021

Keywords

configuration
evaluation
feature models
recommender systems

Access to Document

10.1145/3461001.3471144

https://splc2021.net

Cite this

Uta, M., Felfernig, A., Le, V. M., Popescu, A., Tran, T. N. T., & Helic, D. (2021). Evaluating recommender systems in feature model configuration. In M. Mousavi, P.-Y. Schobbens, H. Araujo, I. Schaefer, M. H. ter Beek, X. Devroey, J. M. Rojas, M. Pinto, L. Teixeira, T. Berger, J. Noppen, I. Reinhartz-Berger, P. Temple, F. Damiani, & J. Petke (Eds.), Proceedings of the 25th ACM International Systems and Software Product Line Conference, SPLC 2021 (pp. 58-63). Association of Computing Machinery. https://doi.org/10.1145/3461001.3471144

Uta, Mathias ; Felfernig, Alexander ; Le, Viet Man et al. / Evaluating recommender systems in feature model configuration. Proceedings of the 25th ACM International Systems and Software Product Line Conference, SPLC 2021. editor / Mohammad Mousavi ; Pierre-Yves Schobbens ; Hugo Araujo ; Ina Schaefer ; Maurice H. ter Beek ; Xavier Devroey ; Jose Miguel Rojas ; Monica Pinto ; Leopoldo Teixeira ; Thorsten Berger ; Johannes Noppen ; Iris Reinhartz-Berger ; Paul Temple ; Ferruccio Damiani ; Justyna Petke. United States : Association of Computing Machinery, 2021. pp. 58-63

@inproceedings{ee9fa08c92314eb0a014bde3f19aba81,

title = "Evaluating recommender systems in feature model configuration.",

abstract = "Configurators can be evaluated in various ways such as efficiency and completeness of solution search, optimality of the proposed solutions, usability of configurator user interfaces, and configuration consistency. Due to the increasing size and complexity of feature models, the integration of recommendation algorithms with feature model configurators becomes relevant. In this paper, we show how the output of a recommender system can be evaluated within the scope of feature model configuration scenarios. Overall, we argue that the discussed ways of measuring recommendation quality help developers to gain a broader view on evaluation techniques in constraint-based recommendation domains.",

keywords = "configuration, evaluation, feature models, recommender systems",

author = "Mathias Uta and Alexander Felfernig and Le, {Viet Man} and Andrei Popescu and Tran, {Thi Ngoc Trang} and Denis Helic",

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booktitle = "Proceedings of the 25th ACM International Systems and Software Product Line Conference, SPLC 2021",

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Uta, M, Felfernig, A, Le, VM, Popescu, A, Tran, TNT & Helic, D 2021, Evaluating recommender systems in feature model configuration. in M Mousavi, P-Y Schobbens, H Araujo, I Schaefer, MH ter Beek, X Devroey, JM Rojas, M Pinto, L Teixeira, T Berger, J Noppen, I Reinhartz-Berger, P Temple, F Damiani & J Petke (eds), Proceedings of the 25th ACM International Systems and Software Product Line Conference, SPLC 2021. Association of Computing Machinery, United States, pp. 58-63, 25th ACM International Systems and Software Product Line Conference, 06/09/2021. https://doi.org/10.1145/3461001.3471144

Evaluating recommender systems in feature model configuration. / Uta, Mathias; Felfernig, Alexander; Le, Viet Man et al.
Proceedings of the 25th ACM International Systems and Software Product Line Conference, SPLC 2021. ed. / Mohammad Mousavi; Pierre-Yves Schobbens; Hugo Araujo; Ina Schaefer; Maurice H. ter Beek; Xavier Devroey; Jose Miguel Rojas; Monica Pinto; Leopoldo Teixeira; Thorsten Berger; Johannes Noppen; Iris Reinhartz-Berger; Paul Temple; Ferruccio Damiani; Justyna Petke. United States: Association of Computing Machinery, 2021. p. 58-63.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Le, Viet Man

AU - Popescu, Andrei

AU - Tran, Thi Ngoc Trang

AU - Helic, Denis

N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 25th ACM International Systems and Software Product Line Conference : SPLC'21, SPLC'21 ; Conference date: 06-09-2021 Through 11-09-2021

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AB - Configurators can be evaluated in various ways such as efficiency and completeness of solution search, optimality of the proposed solutions, usability of configurator user interfaces, and configuration consistency. Due to the increasing size and complexity of feature models, the integration of recommendation algorithms with feature model configurators becomes relevant. In this paper, we show how the output of a recommender system can be evaluated within the scope of feature model configuration scenarios. Overall, we argue that the discussed ways of measuring recommendation quality help developers to gain a broader view on evaluation techniques in constraint-based recommendation domains.

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KW - feature models

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BT - Proceedings of the 25th ACM International Systems and Software Product Line Conference, SPLC 2021

A2 - Mousavi, Mohammad

A2 - Schobbens, Pierre-Yves

A2 - Araujo, Hugo

A2 - Schaefer, Ina

A2 - ter Beek, Maurice H.

A2 - Devroey, Xavier

A2 - Rojas, Jose Miguel

A2 - Pinto, Monica

A2 - Teixeira, Leopoldo

A2 - Berger, Thorsten

A2 - Noppen, Johannes

A2 - Reinhartz-Berger, Iris

A2 - Temple, Paul

A2 - Damiani, Ferruccio

A2 - Petke, Justyna

PB - Association of Computing Machinery

CY - United States

T2 - 25th ACM International Systems and Software Product Line Conference

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Uta M, Felfernig A, Le VM, Popescu A, Tran TNT, Helic D. Evaluating recommender systems in feature model configuration. In Mousavi M, Schobbens PY, Araujo H, Schaefer I, ter Beek MH, Devroey X, Rojas JM, Pinto M, Teixeira L, Berger T, Noppen J, Reinhartz-Berger I, Temple P, Damiani F, Petke J, editors, Proceedings of the 25th ACM International Systems and Software Product Line Conference, SPLC 2021. United States: Association of Computing Machinery. 2021. p. 58-63 doi: 10.1145/3461001.3471144

Evaluating recommender systems in feature model configuration.

Abstract

Conference

Keywords

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