Open Access
| Issue |
ITM Web Conf.
Volume 88, 2026
The 2026 International Conference on Artificial Intelligence, Big Data and Computer Science (AIBDCS 2026)
|
|
|---|---|---|
| Article Number | 01035 | |
| Number of page(s) | 5 | |
| Section | Artificial Intelligence, Big Data and Computer Science | |
| DOI | https://doi.org/10.1051/itmconf/20268801035 | |
| Published online | 27 July 2026 | |
- Liu, C., Jiang, D., Cai, Y., & Li, H. (2026). A review of deep learning and large language models for cold start problem in recommender systems. WIREs Data Mining and Knowledge Discovery, 16(1), e70068. https://doi.org/10.1002/widm.70068 [Google Scholar]
- Bie, S., Cao, J., Luo, Z., et al. (2026). PushGen: Push notifications generation with LLM. In Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining (WSDM ‘26). ACM. https://doi.org/10.1145/3773966.3779373 [Google Scholar]
- Sanner, S., et al. (2026). Large language models are competitive near cold-start recommenders for language-based preferences. arXiv preprint / OpenReview. [Google Scholar]
- Zhao, Y., Shen, H., Li, D., et al. (2025). Meta-learning for cold-start personalization in prompt-tuned LLMs. In Proceedings of the IEEE Conference on Big Data and Smart Engineering (CBASE 2025) (pp. 462–467). IEEE. https://arxiv.org/abs/2507.16672 [Google Scholar]
- Taşköprü, H., et al. (2025). Notification text generation with large language models. In Proceedings of the IEEE Signal Processing and Communications Applications Conference (SIU 2025). Istanbul, Turkey. [Google Scholar]
- Zheng, Z., Zhu, Y., Liu, H., et al. (2025). MI4Rec: Pretrained language model based cold-start recommendation with meta-item embeddings. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM ‘25). ACM. [Google Scholar]
- Zhao, Y., et al. (2025). Efficient cold-start recommendation via BPE token-level embedding initialization with LLM. arXiv preprint arXiv:2509.13179. [Google Scholar]
- Halimeh, H., Freese, F., & Müller, O. (2025). LLMs for warm and cold next-item recommendation: A comparative study across zero-shot prompting, in-context learning and fine-tuning. In Proceedings of the International Conference on Information Systems Development (ISD). https://doi.org/10.62036/isd.2025.68 [Google Scholar]
- Wongso, W., et al. (2025). GenUP: Generative user profilers as in-context learners for next POI recommender systems. arXiv preprint arXiv:2410.20643v2. [Google Scholar]
- Liang, X., Nguyen, V., Le, V., et al. (2025). In-context learning for addressing user cold-start in sequential movie recommenders. In Proceedings of the Nineteenth ACM Conference on Recommender Systems (RecSys ‘25). ACM. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.