App Download Forecasting: An Evolutionary Hierarchical Competition Approach
Authors: Yingzi Wang, Nicholas Jing Yuan, Yu Sun, Chuan Qin, Xing Xie
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments using a real-world app download dataset show that EHCM outperforms state-of-the-art methods in various forecasting granularities. |
| Researcher Affiliation | Collaboration | University of Science and Technology of China Microsoft Research Microsoft Corporation University of Melbourne |
| Pseudocode | No | The paper describes the model formulation and optimization steps using mathematical equations and prose, but it does not include any structured pseudocode or algorithm blocks. |
| 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 | No | The paper states, "From a commercial app analysis platform, we collect the download amount data for over 20 thousand apps...", but it does not provide any specific information, links, or citations to make this dataset publicly accessible. |
| Dataset Splits | Yes | We divide each sub-matrix X into a training matrix and testing matrix at different positions for such settings. For example, when we need to evaluate the forecasting accuracy of half a month in the future, we divide each X into X R20,781 350 as the training data and ˆX R20,781 15 as testing data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | We vary the demand category amount M from 5 to 30, and vary the stabilization segment amount L from 7 to 27. Table 1 presents the average results for this experiment when the forecasting period is 15 days (where training period is 350 days). |