Prior-Based Dual Additive Latent Dirichlet Allocation for User-Item Connected Documents
Authors: Wei Zhang, Jianyong Wang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we evaluate PDA-LDA on several real datasets and the results demonstrate that our model is effective in comparison to several other models, including held-out perplexity on modeling text and document classification application. |
| Researcher Affiliation | Academia | Wei Zhang Jianyong Wang Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology, Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1: The Gibbs EM Algorithm for PDA-LDA |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the methodology is openly available. |
| Open Datasets | Yes | We adopt three real data collections from Yelp and [Mc Auley and Leskovec, 2013]. Based on their origins, we denominate the three data sets as Yelp, Amazon Food and Amazon Sport, respectively. |
| Dataset Splits | Yes | We randomly divide the two collections into train, validation, and test set with the ratio 7 : 1 : 2 for testing held-out perplexity and further binary document classification task. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific libraries or frameworks like PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | All the hyper-parameters are determined based on their performances on validation datasets. For all the comparison methods and PDA-LDA, we assign 0.1 to η. For comparisons except AS-LDA, we choose α to be 0.1 as well for its good performance. The concentration parameter α in AS-LDA is tunned to be 0.1. Apart from η, λU, λV , and λb are set to be 1 for PDA-LDA uniformly. |