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 [1].
fastFM: A Library for Factorization Machines
Authors: Immanuel Bayer
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show, that the ALS and MCMC solver in fast FM compare favorable to lib FM with respect to runtime (Figure 2) and are indistinguishable in terms of accuracy. The experiments have been conducted on the Movie Lens 10M data set using the original split with a fixed number of 200 iterations for all experiments. |
| Researcher Affiliation | Academia | Immanuel Bayer EMAIL University of Konstanz 78457 Konstanz , Germany |
| Pseudocode | No | The paper includes Python code snippets demonstrating usage, but no formal pseudocode or algorithm blocks are present. |
| Open Source Code | Yes | fast FM contains a test suite that is run on each commit to the Git Hub repository via a continuous integration server4. https://travis-ci.org/ibayer/fast FM-core |
| Open Datasets | Yes | The experiments have been conducted on the Movie Lens 10M data set using the original split with a fixed number of 200 iterations for all experiments. |
| Dataset Splits | Yes | The experiments have been conducted on the Movie Lens 10M data set using the original split with a fixed number of 200 iterations for all experiments. |
| Hardware Specification | No | The paper discusses runtime comparisons but does not specify the hardware (e.g., CPU, GPU models, memory) on which these experiments were conducted. |
| Software Dependencies | No | The paper mentions software like Cython, CXSparse, and scikit-learn, but does not provide specific version numbers for these dependencies, which are necessary for reproducibility. |
| Experiment Setup | Yes | fm = mcmc.FMClassification(init std=0.01, rank=8) fm = als.FMRegression(init std=0.01, rank=8, l2 reg=2) The experiments have been conducted on the Movie Lens 10M data set using the original split with a fixed number of 200 iterations for all experiments. |