Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations
Authors: Krishna Prasad Neupane, Ervine Zheng, Yu Kong, Qi Yu7868-7876
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments over real-world data help demonstrate the effectiveness of the proposed time-sensitive cold-start recommendation model. |
| Researcher Affiliation | Academia | Rochester Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1: Model training |
| Open Source Code | Yes | Experimental settings, datasets details, and analysis of the above key properties are presented in the Appendix (Neupane et al. 2021). [...] Neupane, K.; Zheng, E.; Kong, Y.; and Yu, Q. 2021. Appendix: A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations. https://github.com/ritmininglab/A-Dynamic-Meta-Learning-Model-for-Time-Sensitive-Cold-Start-Recommendations/blob/main/Appendix.pdf. |
| Open Datasets | Yes | We conduct experiments on two movie datasets: Netflix and Movie Lens-1M that consist of users ratings of movies as explicit feedback and one music dataset: Last.fm that consists of users play counts of music tracks as implicit feedback. |
| Dataset Splits | No | The paper mentions 'meta-train' and 'meta-test' sets but does not explicitly provide the specific percentages or counts for training, validation, and test splits in the main text, deferring details to an appendix. |
| Hardware Specification | No | The paper does not provide any specific hardware details (like GPU models, CPU types, or cloud configurations) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., programming languages, libraries, or frameworks with version tags) used for implementation or experimentation. |
| Experiment Setup | Yes | Require: Hyperparameters: α, β, γ |