A Bayesian Latent Variable Model of User Preferences with Item Context
Authors: Aghiles Salah, Hady W. Lauw
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 {asalah, hadywlauw}@smu.edu.sg |
| 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]. |