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..
Probabilistic Matrix Factorization with Non-random Missing Data
Authors: Jose Miguel Hernandez-Lobato, Neil Houlsby, Zoubin Ghahramani
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze the performance of our Matrix Factorization model with data Missing Not At Random (MF-MNAR) in a series of experiments with synthetic and real-world rating data. |
| Researcher Affiliation | Academia | Jos e Miguel Hern andez-Lobato EMAIL Neil Houlsby EMAIL Zoubin Ghahramani EMAIL University of Cambridge, Department of Engineering, Cambridge CB2 1PZ, UK |
| Pseudocode | Yes | Algorithm 1 Approximate Inference in the Joint Model |
| Open Source Code | Yes | The code for our MF-MNAR method is publicly available at http://jmhl.org. |
| Open Datasets | Yes | The Movie Lens 100k and 1M datasets1 include ratings from 1 to 5 on movies. The Yahoo! Web-scope R3 dataset2. contains ratings from 1 to 5 on songs. We also analyzed a dataset obtained from the reviewer bidding process for the 2013 NIPS conference. Finally, the Movie Tweetings dataset3 includes ratings on movies collected from Twitter. |
| Dataset Splits | No | we randomly split the observed ratings (the ri,j with xi,j = 1) into a training set with 99% of the ratings and a standard test set with the remaining 1%. (No mention of a dedicated validation set.) |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all MF methods we use a latent rank of size 20. In the mixture of multinomials we use 20 components. |