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].
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 |