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..
Polynomial Optimization Methods for Matrix Factorization
Authors: Po-Wei Wang, Chun-Liang Li, J. Kolter
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On three benchmark datasets we show the method substantially improves convergence speed over state-of-the-art approaches, while also attaining lower objective value. |
| Researcher Affiliation | Academia | Po-Wei Wang Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Chun-Liang Li Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 EMAIL J. Zico Kolter School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 EMAIL |
| Pseudocode | Yes | Algorithm 1: Poly MF-CD and Algorithm 2: Poly MF-SS are provided. |
| Open Source Code | No | The paper mentions using existing open-source packages like LIBMF and CCD++, but it does not state that the authors' own implementation code for the proposed methods (Poly MF-CD, Poly MF-SS) is publicly available. |
| Open Datasets | Yes | We evaluate the Poly MF algorithm (both the coordinate descent and subspace search approaches) on several benchmark problems in recommender systems. In particular, we consider three datasets: Movielens10m, Netflix (Bell and Koren 2007), and Yahoo Music (Dror et al. 2012). |
| Dataset Splits | No | Table 1 provides statistics for Movielens10m, Netflix, and Yahoo-Music, including '|S|' (implied training size) and '|Stest|' (test set size). However, there is no explicit mention or size given for a separate 'validation' dataset split. |
| Hardware Specification | Yes | We conduct the experiments on a Intel Core i7-4790 machine with 32 GB memory. |
| Software Dependencies | No | The paper mentions using 'LIBMF' and 'CCD++' packages for comparison and as a basis for their method, but it does not specify version numbers for these or any other software libraries or programming environments used in their experiments. |
| Experiment Setup | Yes | We choose regularization parameter λ as shown in Table 1, and focus our analysis on the optimization performance compared to the following algorithms:... if func_decr < 10-8max_func_decr then break |