Towards a Technical Debt for AI-based Recommender System

Sergio Moreschini; Ludovik Coba; Valentina Lenarduzzi

doi:10.1145/3644384.3648574

Towards a Technical Debt for AI-based Recommender System

Sergio Moreschini, Ludovik Coba, Valentina Lenarduzzi

Computing Sciences

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

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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.

Original language	English
Title of host publication	Proceedings - 2024 ACM/IEEE International Conference on Technical Debt, TechDebt 2024
Publisher	ACM
Pages	36-39
Number of pages	4
ISBN (Electronic)	979-8-4007-0590-8
DOIs	https://doi.org/10.1145/3644384.3648574
Publication status	Published - 14 Apr 2024
Publication type	A4 Article in conference proceedings
Event	ACM/IEEE International Conference on Technical Debt, co-located with the International Conference on Software Engineering - Lisbon, Portugal Duration: 14 Apr 2024 → 15 Apr 2024

Publication series

Name	Proceedings - 2024 ACM/IEEE International Conference on Technical Debt, TechDebt 2024

Conference

Conference	ACM/IEEE International Conference on Technical Debt, co-located with the International Conference on Software Engineering
Country/Territory	Portugal
City	Lisbon
Period	14/04/24 → 15/04/24

Keywords

Artificial Intelligence
Machine Learning
Recommender System
Technical Debt

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Management of Technology and Innovation
Hardware and Architecture
Software

Access to Document

10.1145/3644384.3648574Licence: CC BY

3644384.3648574Final published version, 459 KBLicence: CC BY

https://urn.fi/URN:NBN:fi:tuni-202409308998Licence: CC BY

Cite this

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title = "Towards a Technical Debt for AI-based Recommender System",

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.",

keywords = "Artificial Intelligence, Machine Learning, Recommender System, Technical Debt",

author = "Sergio Moreschini and Ludovik Coba and Valentina Lenarduzzi",

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language = "English",

series = "Proceedings - 2024 ACM/IEEE International Conference on Technical Debt, TechDebt 2024",

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pages = "36--39",

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Moreschini, S, Coba, L & Lenarduzzi, V 2024, Towards a Technical Debt for AI-based Recommender System. in Proceedings - 2024 ACM/IEEE International Conference on Technical Debt, TechDebt 2024. Proceedings - 2024 ACM/IEEE International Conference on Technical Debt, TechDebt 2024, ACM, pp. 36-39, ACM/IEEE International Conference on Technical Debt, co-located with the International Conference on Software Engineering, Lisbon, Portugal, 14/04/24. https://doi.org/10.1145/3644384.3648574

Towards a Technical Debt for AI-based Recommender System. / Moreschini, Sergio; Coba, Ludovik; Lenarduzzi, Valentina.
Proceedings - 2024 ACM/IEEE International Conference on Technical Debt, TechDebt 2024. ACM, 2024. p. 36-39 (Proceedings - 2024 ACM/IEEE International Conference on Technical Debt, TechDebt 2024).

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

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AU - Coba, Ludovik

AU - Lenarduzzi, Valentina

PY - 2024/4/14

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N2 - 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.

AB - 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.

KW - Artificial Intelligence

KW - Machine Learning

KW - Recommender System

KW - Technical Debt

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DO - 10.1145/3644384.3648574

M3 - Conference contribution

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BT - Proceedings - 2024 ACM/IEEE International Conference on Technical Debt, TechDebt 2024

PB - ACM

T2 - ACM/IEEE International Conference on Technical Debt, co-located with the International Conference on Software Engineering

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ER -

Towards a Technical Debt for AI-based Recommender System

Abstract

Publication series

Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

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Cite this