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..
Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence
Authors: Trong Dinh Thac Do, Longbing Cao
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that m GDMF significantly (both effectively and efficiently) outperforms the state-of-the-art static and dynamic models on large, sparse and dynamic data. |
| Researcher Affiliation | Academia | Trong Dinh Thac Do Advanced Analytics Institute University of Technology Sydney EMAIL Longbing Cao Advanced Analytics Institute University of Technology Sydney EMAIL |
| Pseudocode | Yes | Algorithm 1 SVI for m GDMF |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology. |
| Open Datasets | Yes | Netflix-Time. Similar procedure as in [37, 16, 34] is taken to obtain a subset of Netflix Prize data [4]... Yelp-Active. A subset of the Yelp Academic Challenge data is obtained similarly to [34]... LFM-Tracks. It contains the number of times a user listened to a song during a given time period [12]: 16 time slices of 0.9K users and 1M tracks (i.e., songs), similar to [34]; |
| Dataset Splits | Yes | We then randomly sample and assign 5% of the test set for validation, similar to [16, 34]. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | For the static portions, we set a = b = c = d = 0.3 in the same way as in HPF. The metadata hyper-parameters a , b , c and d are set to a small value: 0.1, so that the user/item attribute weights automatically grow over time. We also set aθ = aγ = aθ = bθ = bβ = aι = 1 to keep the chains small at the beginning. We test a wide range of latent components K from 10 to 200 and choose the best K = 50 for m GDMF/GDMF. For SVI hyper-parameters, we assign 10, 000 as the learning rate delay iter0 and 0.7 as the learning rate power ϵ, similar to [34] and [3]. |