Improved Bounded Matrix Completion for Large-Scale Recommender Systems
Authors: Huang Fang, Zhang Zhen, Yiqun Shao, Cho-Jui Hsieh
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real world datasets show that our algorithm can reach a lower objective function value, obtain a higher prediction accuracy and have better scalability compared with existing bounded matrix completion approaches. |
| Researcher Affiliation | Academia | Huang Fang1, Zhen Zhang1, Yiqun Shao2, Cho-Jui Hsieh1 1Departments of Statistics and Computer Science 2Department of Mathematics University of California, Davis {hgfang, ezzhang, yqshao, chohsieh}@ucdavis.edu |
| Pseudocode | Yes | Algorithm 1 BMC-ADMM for Bounded Matrix Completion |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We use five real world datasets to test the performance of the above matrix completion algorithms, and the detailed data statistics are listed in Table 1. ... dataset m n |Ω| range movielens100k 671 9,066 100,004 [0.5, 5] movielens10m 71,567 10,677 10,000,054 [0.5, 5] Flixster subset 14,761 4,903 81,495 [0.5, 5] Flixster 147,612 48,794 8,196,077 [0.5, 5] Jester 50,692 150 1,728,847 [-10, 10] |
| Dataset Splits | No | We randomly split 80% as training data and 20% as testing data. For each algorithm and dataset, we choose the best regularization parameters from λ {0, 0.01, 0.1, 1, 10, 100} based on validation set. However, the paper does not specify the exact split percentage or size of the validation set. |
| Hardware Specification | Yes | All the following experiments are conducted on an Intel i5-4590 3.30 GHz CPU with 16GB RAM. |
| Software Dependencies | No | The paper mentions using PROPACK for SVD computation but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set ρ1 = ρ2 = 1 and try λ = 0.1 and 10, and compare the performance on all the datasets. For each algorithm and dataset, we choose the best regularization parameters from λ {0, 0.01, 0.1, 1, 10, 100} based on validation set. To have a fair comparison, we tried different settings of rank k = 5, 10, 30 for all the algorithms. |