A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations
Authors: Krishna Prasad Neupane, Ervine Zheng, Yu Kong, Qi Yu7868-7876
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 {kpn3569, mxz5733, yu.kong, qi.yu}@rit.edu |
| 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 |