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 [1].
Learning User Dependencies for Recommendation
Authors: Yong Liu, Peilin Zhao, Xin Liu, Min Wu, Lixin Duan, Xiao-Li Li
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four real datasets have been performed to demonstrate the effectiveness of the proposed PRMF model. |
| Researcher Affiliation | Collaboration | Institute for Infocomm Research, A*STAR, Singapore Artificial Intelligence Department, Ant Financial Services Group, China Garena Online, Singapore Big Data Research Center, University of Electronic Science and Technology of China, China |
| Pseudocode | Yes | Algorithm 1: PRMF Optimization Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements or links to open-source code for the methodology described. |
| Open Datasets | Yes | The experiments are performed on four public datasets: Movie Lens-100K, Movie Lens-1M3, Ciao, and Epinions4. |
| Dataset Splits | Yes | Cross-validation is adopted to choose the parameters for each evaluated algorithm. The validation data is constructed by randomly chosen 10% of the ratings in the training data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software components with their version numbers. |
| Experiment Setup | Yes | For matrix factorization methods, the dimensionality of latent space d is set to 10. The latent features of users and items are randomly initialized by a Gaussian distribution with mean 0 and standard deviation 1/d. Moreover, we set the regularization parameters λu = λv and choose the parameters from {10-5, 10-4, ..., 10-1}. For PRMF, α is chosen from {2-5, 2-4, ..., 2-1}, θ is chosen from {2-5, 2-4, ..., 2-1}. For simplicity, we empirically set γ = 10-4, β = 10, and ρ = 100. |