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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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)... |