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..
A Bayesian Latent Variable Model of User Preferences with Item Context
Authors: Aghiles Salah, Hady W. Lauw
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on real-world datasets show evident performance improvements over strong factorization models. |
| Researcher Affiliation | Academia | Aghiles Salah and Hady W. Lauw School of Information Systems, Singapore Management University, Singapore EMAIL |
| Pseudocode | Yes | Algorithm 1 Variational inference for C2PF. |
| 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 | Yes | We use six datasets from Amazon.com2, provided by Mc Auley et al.; Mc Auley et al. [2015b; 2015a]. These datasets include both the user-item preferences and the Also Viewed lists that we treat as the item contexts. (...) 2http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | No | The paper states 'For each dataset, we randomly select 80% of the ratings as training data and the remaining 20% as test data' but does not explicitly mention a separate validation set or split for hyperparameter tuning. |
| Hardware Specification | No | The paper discusses computational complexity but does not provide specific details about the hardware (e.g., GPU/CPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers required to reproduce the experiments. |
| Experiment Setup | Yes | For most experiments, we set the number of latent components K to 100. (...) To encourage sparse latent representations, we set αθ = αβ = αξ = (0.3, 0.3) resulting in exponentially shaped Gamma distributions with mean equal to 1. We further set δ = (2, 5) and αs κ = 2, fixing the prior mean over the context effects to 0.5. (...) We initialize the Gamma variational parameters, λs and λr, to a small random perturbation of the corresponding prior parameters. (...) To set the different hyperparameters of MCF, we follow the same strategy, grid search, as in [Park et al., 2017]. |