Analysis Operations for Constraint-based Recommender Systems

Sebastian Lubos; Viet-Man Le; Alexander Felfernig; Thi Ngoc Trang Tran

doi:10.1145/3604915.3608819

Analysis Operations for Constraint-based Recommender Systems

Sebastian Lubos^*, Viet-Man Le, Alexander Felfernig, Thi Ngoc Trang Tran

^*Corresponding author for this work

Institute of Software Engineering and Artificial Intelligence (7160)

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

Abstract

Constraint-based recommender systems support users in the identification of complex items such as financial services and digital cameras (digicams). Such recommender systems enable users to find an appropriate item within the scope of a conversational process. In this context, relevant items are determined by matching user preferences with a corresponding product (item) assortment on the basis of a pre-defined set of constraints. The development and maintenance of constraint-based recommenders is often an error-prone activity – specifically with regard to the scoping of the offered item assortment. In this paper, we propose a set of offline analysis operations (metrics) that provide insights to assess the quality of a constraint-based recommender system before the system is deployed for productive use. The operations include a.o. automated analysis of feature restrictiveness and item (product) accessibility. We analyze usage scenarios of the proposed analysis operations on the basis of a simplified example digicam recommender.

Original language	English
Title of host publication	RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems
Place of Publication	New York, NY
Publisher	Association of Computing Machinery
Pages	709–714
Number of pages	6
ISBN (Electronic)	979-8-4007-0241-9
DOIs	https://doi.org/10.1145/3604915.3608819
Publication status	Published - 14 Sept 2023
Event	17th ACM Conference on Recommender Systems: RecSys 2023 - Singapore, Singapore Duration: 18 Sept 2023 → 22 Sept 2023 https://recsys.acm.org/recsys23/

Conference

Conference	17th ACM Conference on Recommender Systems
Abbreviated title	RecSys'23
Country/Territory	Singapore
City	Singapore
Period	18/09/23 → 22/09/23
Internet address	https://recsys.acm.org/recsys23/

Keywords

Constraint-based recommender systems
evaluating recommender systems
evaluation metrics

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Software
Control and Systems Engineering

Access to Document

10.1145/3604915.3608819Licence: CC BY 4.0

3604915.3608819Final published version, 556 KBLicence: CC BY 4.0

Cite this

Lubos, S , Le, V-M , Felfernig, A & Tran, TNT 2023, Analysis Operations for Constraint-based Recommender Systems. in RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems. Association of Computing Machinery, New York, NY, pp. 709–714, 17th ACM Conference on Recommender Systems, Singapore, Singapore, 18/09/23. https://doi.org/10.1145/3604915.3608819

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abstract = "Constraint-based recommender systems support users in the identification of complex items such as financial services and digital cameras (digicams). Such recommender systems enable users to find an appropriate item within the scope of a conversational process. In this context, relevant items are determined by matching user preferences with a corresponding product (item) assortment on the basis of a pre-defined set of constraints. The development and maintenance of constraint-based recommenders is often an error-prone activity – specifically with regard to the scoping of the offered item assortment. In this paper, we propose a set of offline analysis operations (metrics) that provide insights to assess the quality of a constraint-based recommender system before the system is deployed for productive use. The operations include a.o. automated analysis of feature restrictiveness and item (product) accessibility. We analyze usage scenarios of the proposed analysis operations on the basis of a simplified example digicam recommender.",

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author = "Sebastian Lubos and Viet-Man Le and Alexander Felfernig and Tran, {Thi Ngoc Trang}",

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AU - Tran, Thi Ngoc Trang

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PY - 2023/9/14

Y1 - 2023/9/14

N2 - Constraint-based recommender systems support users in the identification of complex items such as financial services and digital cameras (digicams). Such recommender systems enable users to find an appropriate item within the scope of a conversational process. In this context, relevant items are determined by matching user preferences with a corresponding product (item) assortment on the basis of a pre-defined set of constraints. The development and maintenance of constraint-based recommenders is often an error-prone activity – specifically with regard to the scoping of the offered item assortment. In this paper, we propose a set of offline analysis operations (metrics) that provide insights to assess the quality of a constraint-based recommender system before the system is deployed for productive use. The operations include a.o. automated analysis of feature restrictiveness and item (product) accessibility. We analyze usage scenarios of the proposed analysis operations on the basis of a simplified example digicam recommender.

AB - Constraint-based recommender systems support users in the identification of complex items such as financial services and digital cameras (digicams). Such recommender systems enable users to find an appropriate item within the scope of a conversational process. In this context, relevant items are determined by matching user preferences with a corresponding product (item) assortment on the basis of a pre-defined set of constraints. The development and maintenance of constraint-based recommenders is often an error-prone activity – specifically with regard to the scoping of the offered item assortment. In this paper, we propose a set of offline analysis operations (metrics) that provide insights to assess the quality of a constraint-based recommender system before the system is deployed for productive use. The operations include a.o. automated analysis of feature restrictiveness and item (product) accessibility. We analyze usage scenarios of the proposed analysis operations on the basis of a simplified example digicam recommender.

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ER -

Analysis Operations for Constraint-based Recommender Systems

Abstract

Conference

Keywords

ASJC Scopus subject areas

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