Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Scalable Maximum Margin Matrix Factorization by Active Riemannian Subspace Search
Authors: Yan Yan, Mingkui Tan, Ivor Tsang, Yi Yang, Chengqi Zhang, Qinfeng Shi
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 17,762 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,529 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on both synthetic data sets and large real-world data sets demonstrate the superior efficiency and effectiveness of the proposed method.4 Empirical Studies We demonstrate the performance of the proposed methods, namely BNRCG-M3F with fixed-rank problems and ARSS-M3F, by comparing with several related state-of-theart methods, including FM3F [Rennie and Srebro, 2005], GROUSE [Balzano et al., 2010], LMAFIT [Wen et al., 2012], Sc Grass MC [Ngo and Saad, 2012], LRGeom CG [Vandereycken, 2013] and RTRMC [Boumal and Absil, 2011] , on both synthetic and real-world CF tasks. |
| Researcher Affiliation | Academia | 1Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney, Australia 2Australian Centre for Visual Technologies, The University of Adelaide, Australia |
| Pseudocode | Yes | Algorithm 1 BNRCG for Fixed-rank M3F. and Algorithm 2 Active Riemannian Subspace Search for M3F . |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Seven data sets are used in the experiments, including three synthetic data sets and four real-world data sets, Movielens 1M, Movielens 10M [Herlocker et al., 1999], Netflix [Bennett and Lanning, 2007] and Yahoo! Music Track 1 data set [Dror et al., 2012]. |
| Dataset Splits | Yes | For each data set, we sample 80% of data into the training set and the rest into the testing set. |
| Hardware Specification | Yes | All the experiments are conducted in Matlab on a work station with an Intel(R) CPU ( Xeon(R) E5-2690 v2 @ 3.00GHz) and 256GB memory. |
| Software Dependencies | No | The paper states that experiments were conducted in "Matlab" but does not provide a specific version number or list other software dependencies with their versions. |
| Experiment Setup | Yes | Additionally, to prevent from over-fitting, we regularize ℓ(X, θ) by a regularizer Υ(X) = 1 2(||X||2 F + ν||X ||2 F ), where X denotes the pseudo-inverse and ν > 0 is a small scalar (e.g., ν = 0.0001 in this paper by default)... |