Learning Discriminative Recommendation Systems with Side Information
Authors: Feipeng Zhao, Yuhong Guo
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed approach on a number of recommendation datasets. The experimental results show that the proposed joint model is very effective for producing top-N recommendation systems; We conducted experiments on a few real world datasets. |
| Researcher Affiliation | Academia | Feipeng Zhao Computer and Information Sciences Temple University, Philadelphia, USA feipeng.zhao@temple.edu Yuhong Guo School of Computer Science Carleton University, Ottawa, Canada yuhong.guo@carleton.ca |
| Pseudocode | Yes | Algorithm 1 Projected Gradient Descent Algorithm |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository for the described methodology. |
| Open Datasets | No | The paper mentions using 'the real world Amazon user rating and product review data for five categories of products' and that 'the original dataset we downloaded' was used. However, it does not provide concrete access information such as a specific link, DOI, repository name, or a formal citation with authors and year for public access to this dataset. |
| Dataset Splits | Yes | We evaluated our proposed method and the comparison methods using 5-fold Leave-One-Out Cross-Validation. For each fold, the dataset is divided into a training set and a testing set: We randomly chose one transaction for each user and placed it to the test set, and the rest is used as training set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers that would be needed to replicate the experiment. |
| Experiment Setup | Yes | The default N value used in our experiments is 10. The params columns contain the parameter setting for each approach. Pure SVD has one parameter f, indicating the number of latent factors. WRMF has two parameters, the regularization parameter λ and the latent factor dimension f. SLIM has two parameters, the ℓ2 and ℓ1 norm regularization parameters β and λ. Beyond β and λ, c SLIM has an additional side information weight parameter α. IMC has two parameters, hidden dimension f and regularization parameter λ. The proposed method has two trade-off parameters, β and γ. |