Probabilistic Matrix Factorization with Non-random Missing Data
Authors: Jose Miguel Hernandez-Lobato, Neil Houlsby, Zoubin Ghahramani
ICML 2014 | Conference PDF | Archive PDF | Plain Text | 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 JMH233@CAM.AC.UK Neil Houlsby NMTH2@CAM.AC.UK Zoubin Ghahramani ZOUBIN@ENG.CAM.AC.UK 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. |