Towards a Technical Debt for 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 Recommender Systems. We identified 15 potential factors, along with detailed explanations outlining why it is advisable to consider them.
- Multistakeholder recommendation: Survey and research directions. User Modeling and User-Adapted Interaction 30 (2020), 127–158.
- Code Smells for Multi-Language Systems. In European Conference on Pattern Languages of Programs. Article 12, 13Â pages.
- Mouna Abidi and Foutse Khomh. 2020. Towards the Definition of Patterns and Code Smells for Multi-Language Systems. In European Conference on Pattern Languages of Programs 2020. Article 37.
- Artwork personalization at Netflix. In Proceedings of the 12th ACM conference on recommender systems. 487–488.
- Microsoft recommenders: Best practices for production-ready recommendation systems. In Companion Proceedings of the Web Conference 2020. 50–51.
- Managing Technical Debt in Software Engineering (Dagstuhl Seminar 16162). Dagstuhl Reports 6, 4 (2016), 110–138.
- Immanuel Bayer. 2016. fastfm: A library for factorization machines. The Journal of Machine Learning Research 17, 1 (2016), 6393–6397.
- How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In Proceedings of the 12th ACM conference on recommender systems. 224–232.
- 10 Years of Technical Debt Research and Practice: Past, Present, and Future. IEEE Software 38, 6 (2021), 24–29.
- RecoXplainer: a library for development and offline evaluation of explainable recommender systems. IEEE Computational Intelligence Magazine 17, 1 (2022), 46–58.
- Ludovik Çoba and Markus Zanker. 2017. Replication and reproduction in recommender systems research-evidence from a case-study with the rrecsys library. In Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, June 27-30, 2017, Proceedings, Part I 30. Springer, 305–314.
- Ward Cunningham. 1992. The WyCash Portfolio Management System. SIGPLAN OOPS Mess. 4, 2 (Dec. 1992), 29–30.
- Rethinking the recommender research ecosystem: reproducibility, openness, and lenskit. In Proceedings of the fifth ACM conference on Recommender systems. 133–140.
- Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM conference on recommender systems. 101–109.
- A survey on concept drift adaptation. ACM computing surveys (CSUR) 46, 4 (2014), 1–37.
- Offline and online evaluation of news recommender systems at swissinfo. ch. In Proceedings of the 8th ACM Conference on Recommender systems. 169–176.
- Recommender systems: an introduction. Cambridge University Press.
- Degenerate feedback loops in recommender systems. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. 383–390.
- A large-scale empirical study of just-in-time quality assurance. IEEE Transactions on Software Engineering 39 (2012).
- Do offline metrics predict online performance in recommender systems? arXiv preprint arXiv:2011.07931 (2020).
- A systematic literature review on Technical Debt prioritization: Strategies, processes, factors, and tools. Journal of Systems and Software 171 (2021), 110827.
- On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem. In International Conference on Technical Debt (TechDebt). 98–107.
- A systematic mapping study on technical debt and its management. Journal of Systems and Software 101 (2015), 193–220.
- Monolith: real time recommendation system with collisionless embedding table. arXiv preprint arXiv:2209.07663 (2022).
- Towards a roadmap on software engineering for responsible AI. In Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI. 101–112.
- Feedback loop and bias amplification in recommender systems. In Proceedings of the 29th ACM international conference on information & knowledge management. 2145–2148.
- Investigating Architectural Technical Debt accumulation and refactoring over time: A multiple-case study. Information and Software Technology 67 (2015), 237 – 253.
- Tf-ranking: Scalable tensorflow library for learning-to-rank. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2970–2978.
- Shaina Raza and Chen Ding. 2019. Progress in context-aware recommender systems—An overview. Computer Science Review 31 (2019), 84–97.
- Recommender systems: introduction and challenges. Recommender systems handbook (2015), 1–34.
- Hidden technical debt in machine learning systems. Advances in neural information processing systems 28 (2015).
- Guy Shani and Asela Gunawardana. 2011. Evaluating recommendation systems. Recommender systems handbook (2011), 257–297.
- Deep learning for recommender systems: A Netflix case study. AI Magazine 42, 3 (2021), 7–18.
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