[{"main_file_link":[{"open_access":"1"}],"language":[{"iso":"eng"}],"_id":"63524","publisher":"University of Gdansk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences","user_id":"87673","doi":"10.62036/isd.2025.68","title":"LLMs For Warm and Cold Next-Item Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning","status":"public","year":"2025","author":[{"id":"87673","full_name":"Halimeh, Haya","last_name":"Halimeh","first_name":"Haya"},{"full_name":"Freese, Florian","last_name":"Freese","first_name":"Florian"},{"full_name":"Müller, Oliver","last_name":"Müller","first_name":"Oliver"}],"publication_identifier":{"issn":["2938-5202"]},"publication_status":"published","date_updated":"2026-01-07T13:47:43Z","date_created":"2026-01-07T13:36:53Z","type":"conference","department":[{"_id":"195"},{"_id":"196"}],"oa":"1","publication":"International Conference on Information Systems Development","citation":{"chicago":"Halimeh, Haya, Florian Freese, and Oliver Müller. “LLMs For Warm and Cold Next-Item Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning.” In <i>International Conference on Information Systems Development</i>. University of Gdansk, Department of Business Informatics &#38; University of Belgrade, Faculty of Organizational Sciences, 2025. <a href=\"https://doi.org/10.62036/isd.2025.68\">https://doi.org/10.62036/isd.2025.68</a>.","short":"H. Halimeh, F. Freese, O. Müller, in: International Conference on Information Systems Development, University of Gdansk, Department of Business Informatics &#38; University of Belgrade, Faculty of Organizational Sciences, 2025.","apa":"Halimeh, H., Freese, F., &#38; Müller, O. (2025). LLMs For Warm and Cold Next-Item Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning. <i>International Conference on Information Systems Development</i>. <a href=\"https://doi.org/10.62036/isd.2025.68\">https://doi.org/10.62036/isd.2025.68</a>","ieee":"H. Halimeh, F. Freese, and O. Müller, “LLMs For Warm and Cold Next-Item Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning,” 2025, doi: <a href=\"https://doi.org/10.62036/isd.2025.68\">10.62036/isd.2025.68</a>.","ama":"Halimeh H, Freese F, Müller O. LLMs For Warm and Cold Next-Item Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning. In: <i>International Conference on Information Systems Development</i>. University of Gdansk, Department of Business Informatics &#38; University of Belgrade, Faculty of Organizational Sciences; 2025. doi:<a href=\"https://doi.org/10.62036/isd.2025.68\">10.62036/isd.2025.68</a>","bibtex":"@inproceedings{Halimeh_Freese_Müller_2025, title={LLMs For Warm and Cold Next-Item Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning}, DOI={<a href=\"https://doi.org/10.62036/isd.2025.68\">10.62036/isd.2025.68</a>}, booktitle={International Conference on Information Systems Development}, publisher={University of Gdansk, Department of Business Informatics &#38; University of Belgrade, Faculty of Organizational Sciences}, author={Halimeh, Haya and Freese, Florian and Müller, Oliver}, year={2025} }","mla":"Halimeh, Haya, et al. “LLMs For Warm and Cold Next-Item Recommendation: A Comparative Study across Zero-Shot Prompting, In-Context Learning and Fine-Tuning.” <i>International Conference on Information Systems Development</i>, University of Gdansk, Department of Business Informatics &#38; University of Belgrade, Faculty of Organizational Sciences, 2025, doi:<a href=\"https://doi.org/10.62036/isd.2025.68\">10.62036/isd.2025.68</a>."},"abstract":[{"text":"Recommendation systems are essential for delivering personalized content across e-commerce and streaming services. However, traditional methods often fail in cold-start scenarios where new items lack prior interactions. Recent advances in large language models (LLMs) offer a promising alternative. In this paper, we adopt the retrieve-and-recommend framework and propose to fine-tune the LLM jointly on warm-and cold-start next-item recommendation tasks, thus, mitigating the need for separate models for both item types. We computationally compare zero-shot prompting, in-context learning, and fine-tuning using the same LLM backbone, and benchmark them against strong PLM-based baselines. Our findings provide practical insights into the trade-offs between accuracy and computational cost of these methods for next-item recommendation. To enhance reproducibility, we release the source code under https://github. com/HayaHalimeh/LLMs-For-Next-Item-Recommendation.git.","lang":"eng"}]}]