Karl Audun Kagnes Borgersen

• Borgersen, Karl Audun Kagnes; Goodwin, Morten; Sharma, Jivitesh; Aasmoe, Tobias; Leonhardsen, Mari & Rørvik, Gro Herredsvela (2023). CorrEmbed: Evaluating Pre-trained Model Image Similarity Efficacy with a Novel Metric . In Bramer, Max & Stahl, Frederic (Ed.), Artificial Intelligence XL: 43rd SGAI International Conference on Artificial Intelligence, AI 2023. Springer. ISSN 978-3-031-47993-9. p. 89–102. doi: 10.1007/978-3-031-47994-6_7.
• Borgersen, Karl Audun Kagnes; Goodwin, Morten & Sharma, Jivitesh (2023). A comparison between Tsetlin machines and deep neural networks in the context of recommendation systems. Proceedings of the Northern Lights Deep Learning Workshop. doi: 10.7557/18.6807. Show summary

  Recommendation Systems (RSs) are ubiquitous inmodern society and are one of the largest points ofinteraction between humans and AI. Modern RSsare often implemented using deep learning models,which are infamously difficult to interpret. Thisproblem is particularly exasperated in the contextof recommendation scenarios, as it erodes the user’strust in the RS. In contrast, the newly introducedTsetlin Machines (TM) possess some valuable prop-erties due to their inherent interpretability. TMsare still fairly young as a technology. As no RShas been developed for TMs before, it has becomenecessary to perform some preliminary research re-garding the practicality of such a system. In thispaper, we develop the first RS based on TMs toevaluate its practicality in this application domain.This paper compares the viability of TMs withother machine learning models prevalent in the fieldof RS. We train and investigate the performance ofthe TM compared with a vanilla feed-forward deeplearning model. These comparisons are based onmodel performance, interpretability/explainability,and scalability. Further, we provide some bench-mark performance comparisons to similar machinelearning solutions relevant to RSs.

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• Borgersen, Karl Audun Kagnes; Goodwin, Morten & Sharma, Jivitesh (2023). Automating Tagging for Fashion Items: A User Study.

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