Matrix Completion in the Unit Hypercube via Structured Matrix Factorization
Authors: Emanuele Bugliarello, Swayambhoo Jain, Vineeth Rakesh
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the effectiveness of our proposed models by extensive numerical tests on our VFX dataset and two additional datasets with values that are also bounded in the [0, 1] interval. |
| Researcher Affiliation | Collaboration | Emanuele Bugliarello1 , Swayambhoo Jain2 and Vineeth Rakesh2 1Tokyo Institute of Technology 2Technicolor AI Lab |
| Pseudocode | No | The paper describes algorithms but does not present them in structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Experimental results on public data are available on Git Hub 1. URL: https://github.com/e-bug/unit-mf. |
| Open Datasets | Yes | In our third application, we consider the task of placing online advertisements in categories of websites (e.g., Entertainment, Finance, etc.) to maximize their click-through rate. To simulate this scenario, we use publicly available data from a Kaggle competition 2 run by the advertisement company Outbrain. This dataset contains users webpage views and clicks on multiple publisher sites in the United States in a two-week period in June 2016. URL: https://www.kaggle.com/c/outbrain-click-prediction. |
| Dataset Splits | Yes | We use 3 rounds of Monte Carlo cross-validation on the movie production data (due to few non-missing entries) and 3-fold cross-validation on the OTT and CTR data. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper describes the learning algorithms used but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We use a maximum number of 100 epochs and a tolerance deļ¬ned by L(t) L(t 1) L(t) < 10 6 as stopping criteria for training, where L(t) is the objective cost at epoch t. In all SGDbased algorithms, we use batches of 8 entries on the VFX data, and of 128 entries on the OTT and CTR data. The best number of latent factors K is searched over all possible values in the VFX data, while we use the common values of K {10, 15, 20} in the larger OTT and CTR data. Each matrix factor is initialized with uniformly random numbers in (0, 1), and biases are initialized as zero vectors. |