Online Matrix Completion: A Collaborative Approach with Hott Items
Authors: Dheeraj Baby, Soumyabrata Pal
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct synthetic experiments to demonstrate the efficacy of our PHASEDCLUSTERELIM algorithm that runs in phases. All experiments have been conducted on Colab Machines with a system RAM of 12GB and hard disk space of 23.7GB. Simplified Algorithm: Having practical considerations, we propose a simplified version of our PHASEDCLUSTERELIM algorithm (denoted as Algorithm PHASEDELIMSIMPLIFIED ALg. 4). There are two main advantages of Algorithm 4 from implementation point of view 1) the runtime is polynomial in the rank of the reward matrix 2) the number of hyperparameters to tune is much small. |
| Researcher Affiliation | Collaboration | 1Dept. of Computer Science, UC Santa Barbara, California, United States 2Adobe, Bangalore, India. |
| Pseudocode | Yes | Algorithm 1 PHASEDCLUSTERELIM (Iterative Multi-Label User Classification and Joint Item Elimination) |
| Open Source Code | No | 1. For all models and algorithms presented, check if you include: (c) (Optional) Anonymized source code, with specification of all dependencies, including external libraries. [No] |
| Open Datasets | No | We conduct synthetic experiments to demonstrate the efficacy of our PHASEDCLUSTERELIM algorithm that runs in phases. The paper uses synthetic data which is generated according to specific rules, but does not provide access information or citation for a publicly available dataset. |
| Dataset Splits | No | We consider an instance with M = 200 users, N = 200, items, rank r = 4 across T = 300 rounds. Here, we consider the expected reward matrix P = UVT constructed in the following way: ... We run Algorithm PHASEDELIMSIMPLIFIED (Alg. 4 the simplified version of Alg. PHASEDCLUSTERELIM) in this set-up (with an initial exploration period of 25) along with the AM algorithm and the ETC algorithm. The number of exploration rounds in ETC is a hyper-parameter and we run the ETC algorithm with number of exploration rounds m = 25, 50, 75. The paper describes the parameters of its synthetic data generation and exploration periods for its algorithms, but it does not define explicit train/validation/test dataset splits as would be typical for a fixed dataset. |
| Hardware Specification | No | All experiments have been conducted on Colab Machines with a system RAM of 12GB and hard disk space of 23.7GB. While the environment is named ('Colab Machines') and some memory/disk specifications are given, specific CPU or GPU models are not mentioned. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for replication. |
| Experiment Setup | Yes | We consider an instance with M = 200 users, N = 200, items, rank r = 4 across T = 300 rounds. Here, we consider the expected reward matrix P = UVT constructed in the following way: ... with gaussian noise having variance σ2 = 0.25. We run Algorithm PHASEDELIMSIMPLIFIED ... (with an initial exploration period of 25) along with the AM algorithm and the ETC algorithm. The number of exploration rounds in ETC is a hyper-parameter and we run the ETC algorithm with number of exploration rounds m = 25, 50, 75. |