Cold-Start Recommendations for Audio News Stories Using Matrix Factorization
Authors: Ehsan Mohammady Ardehaly, Aron Culotta, Vivek Sundararaman, Alwar Narayanan
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our approach on a dataset of 50K users, 26K stories, and 975K interactions collected over a five month period. |
| Researcher Affiliation | Collaboration | Illinois Institute of Technology, Chicago, IL emohamm1@hawk.iit.edu, aculotta@iit.edu Rivet Radio, Inc., Chicago, IL {vivek.sundararaman,alwar.narayanan}@rivetnewsradio.com |
| Pseudocode | No | The paper describes the algorithms and their mathematical formulations in text, but it does not include any explicit pseudocode blocks or clearly labeled algorithm figures. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses a dataset from 'an existing mobile radio application' and describes its characteristics (50K users, 26K stories, 975K interactions collected over a five month period), but it does not provide concrete access information (e.g., a specific link, DOI, or citation to a public repository/well-known dataset) for this dataset. |
| Dataset Splits | No | The paper describes a temporal splitting strategy for training and testing (e.g., 'train on prior activities to infer the next hour of activity') and how 'user-cold'/'user-warm' and 'item-cold'/'item-warm' quartiles were defined. However, it does not explicitly define distinct validation dataset splits with specific percentages or counts, nor does it refer to standard predefined splits for the dataset used. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU models, GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x). |
| Experiment Setup | No | The paper describes the models and evaluation setup, but it does not provide specific details on hyperparameters (e.g., learning rate, batch size, number of epochs) or other system-level training settings used for the experiments. |