ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems
Authors: DongSheng Li, Chao Chen, Qin Lv, Li Shang, Stephen Chu, Hongyuan Zha
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the Movie Lens and Netflix datasets demonstrate that ERMMA outperforms six state-of-the-art MA-based recommendation methods in both rating prediction problem and item ranking problem. |
| Researcher Affiliation | Collaboration | 1IBM Research China, Shanghai, P.R. China, 201203 2Univeristy of Colorado Boulder, Boulder, Colorado, USA, 80309 3Georgia Institute of Technology, Atlanta, Georgia, USA, 30332 |
| Pseudocode | No | The paper describes iterative optimization methods (e.g., SGD update rules in Equations 7 and 8) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link for the SMA implementation ('https://github.com/ldscc/Stable MA.git') which is a baseline method, but no link or explicit statement about open-sourcing the code for ERMMA. |
| Open Datasets | Yes | Three popular datasets are adopted in the experiments: Movie Lens 1M dataset (6,040 users, 3,706 items, 106 ratings). Movie Lens 10M ( 70k users, 10k items, 107 ratings) and Netflix ( 480k users, 18k items, 108 ratings). |
| Dataset Splits | No | The paper mentions splitting data into 'training and test sets' but does not explicitly state a separate 'validation' split or its proportions. |
| Hardware Specification | No | The paper mentions 'memory limitation of our server on Netflix' but does not specify any exact hardware details such as CPU, GPU models, or memory capacity. |
| Software Dependencies | No | The paper discusses various algorithms and methods but does not specify software dependencies with version numbers (e.g., programming language versions, library versions, or specific solver versions). |
| Experiment Setup | Yes | For ERMMA, we consider all the options including s and λ, and use learning rate v = 0.001 for stochastic gradient decent, μ = 0.06 for regularization coefficient, ϵ = 0.0001 for gradient descent convergence threshold, and T = 250 for maximum number of iterations. For RSVD, BPMF, we use the same parameter values provided in the original papers (Paterek 2007; Salakhutdinov and Mnih 2008; Li et al. 2016). For SMA, all parameters were set to default values in their implementation 1. For GSMF, we select α = 1.0, β = 70, λ = 0.05 and rank r = 20. For LLORMA, we choose learning rate v = 0.001 and regularization coefficient μ = 0.01, and the number of local models z = 50. This is a slight modification of the original LLORMA experimental setup, where better performance can be achieved. For WEMAREC, we adopt learning rate v = 0.002 and regularization coefficient μ = 0.01, and default values provided in the source code for unstated parameters (such as the ensemble weights and the maximum number of iterations in clustering). |