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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |