Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
eTREE: Learning Tree-structured Embeddings
Authors: Faisal M. Almutairi, Yunlong Wang, Dong Wang, Emily Zhao, Nicholas D. Sidiropoulos6609-6617
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the proposed framework on real data from various application domains: healthcare analytics, movie recommendations, and education. This section aims to answer the following questions: Q1. Accuracy: Does e TREE improve the quality of embeddings for the downstream tasks? Q2. Interpretability: How meaningful is the tree structure learned by e TREE from an application domain knowledge viewpoint? Datasets: We evaluate e TREE and the competing baselines on the following real datasets: (i) Med-HF: (ii) Med-MCI: (iii) Movielens: (iv) College Grades: ... Table 1 shows the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of all methods on the different datasets. |
| Researcher Affiliation | Collaboration | 1Department of ECE, University of Minnesota, MN, USA 2Advanced Analytics, IQVIA Inc., Plymouth Meeting, PA, USA 3Department of ECE, University of Virginia, VA, USA EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithmic Solution to e TREE |
| Open Source Code | Yes | 1Code is at: https://github.com/FaisalAlmutairi/e TREE |
| Open Datasets | Yes | Movielens: Movielens (Harper and Konstan 2015) is a movie rating dataset and a popular baseline in recommender systems literature. |
| Dataset Splits | Yes | We split each dataset into 5 equal folds. After training the models on 4 folds (80% of the data), we test the trained models on the held-out fold. The hyper-parameters of all methods are chosen via cross validation (10% of training data). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | in our experiments we set K = 5. ... we set it to ฮท = 1000 in all experiments. ... R = 9, ฮป = 1, ยต = 50 (more emphasis on the tree term), M2 = 27 (number of subcategories), and M3 = 9 (number of main categories). |