Towards a Technical Debt for AI-based Recommender System
Moreschini, Sergio; Coba, Ludovik; Lenarduzzi, Valentina (2024-06-07)
ACM
07.06.2024
Sergio Moreschini, Valentina Lenarduzzi, and Ludovik Coba. 2024. Towards a Technical Debt for AI-based Recommender System. In Proceedings of the 7th ACM/IEEE International Conference on Technical Debt (TechDebt '24). Association for Computing Machinery, New York, NY, USA, 36–39. https://doi.org/10.1145/3644384.3648574
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work licensed under Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work licensed under Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202408085278
https://urn.fi/URN:NBN:fi:oulu-202408085278
Tiivistelmä
Abstract
Balancing the management of technical debt within recommender systems requires effectively juggling the introduction of new features with the ongoing maintenance and enhancement of the current system. Within the realm of recommender systems, technical debt encompasses the trade-offs and expedient choices made during the development and upkeep of the recommendation system, which could potentially have adverse effects on its long-term performance, scalability, and maintainability. In this vision paper, our objective is to kickstart a research direction regarding Technical Debt in AI-based Recommender Systems. We identified 15 potential factors, along with detailed explanations outlining why it is advisable to consider them.
Balancing the management of technical debt within recommender systems requires effectively juggling the introduction of new features with the ongoing maintenance and enhancement of the current system. Within the realm of recommender systems, technical debt encompasses the trade-offs and expedient choices made during the development and upkeep of the recommendation system, which could potentially have adverse effects on its long-term performance, scalability, and maintainability. In this vision paper, our objective is to kickstart a research direction regarding Technical Debt in AI-based Recommender Systems. We identified 15 potential factors, along with detailed explanations outlining why it is advisable to consider them.
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