Metadata Dependent Mondrian Processes
Authors: Yi Wang, Bin Li, Yang Wang, Fang Chen
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically test the proposed MDMP relational model on three real-world data sets with various meta information. We compare MDMP to IRM (Kemp et al., 2006) (block model with Bernoulli distribution in each block) for link prediction, Bi LDA (Porteous et al., 2008) (block model with discrete distribution in each block) for rating prediction, and MP (Roy & Teh, 2009) for both. |
| Researcher Affiliation | Academia | Machine Learning Research Group, National ICT Australia, Eveleigh, NSW 2015, Australia School of Computer Science & Engineering, University of New South Wales, Kensington, NSW 2033, Australia |
| Pseudocode | Yes | The inference framework for MDMP is outlined in Algorithm 1. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | The first data set adopted for link prediction is the Lazega s lawyer data (Lazega, 2003). and We adopt a preprocessed data set (Ma et al., 2011), which comprises 21593 users. and We adopt the Movie Lens data set (https://movielens.org/) for rating prediction. |
| Dataset Splits | Yes | In our experiments, each data set is partitioned into 5 splits, and each time 4 splits are used for training and the rest one is used for testing. and We randomly select 70 users and 70 items from the entire data set and keep the sparsity of the rating matrix being 80% for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For MP and MDMP, we perform 500 iterations of RJMCMC sampling. |