Projective Quadratic Regression for Online Learning
Authors: Wenye Ma5093-5100
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the performance of the proposed PQR model in terms of accuracy and efficiency by comparing with the state-of-the-art methods. |
| Researcher Affiliation | Industry | Wenye Ma Tencent wenyema@tencent.com |
| Pseudocode | Yes | Algorithm 1: Follow-The-Regularized-Leader |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | For the online rating prediction task, we use the typical Movie Lens datasets, including Movie Lens-100K (ML100K), Movie Lens-1M (ML1M) and Movie Lens-10M (ML10M); For the online binary classification task, we select high-dimensional sparse datasets including Avazu, Criteo3, and KDD20124. These three high-dimensional datasets are preprocessed and can be downloaded from the LIBSVM website5. |
| Dataset Splits | Yes | All these datasets are randomly separated into train(80%), validation(10%) and test(10%) sets. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'FTRL-Proximal algorithm' and 'online gradient descent' but does not specify version numbers for any software, libraries, or frameworks used in the implementation. |
| Experiment Setup | Yes | The rank of FM is selected from the set of {2, 4, 8, 16, 32, 64, 128, 256, 512}. The coefficient of regularization term and the learning rate are tuned in a range [0.0001, 10]. |