Enhancing Personalized Trip Recommendation with Attractive Routes
Authors: Jiqing Gu, Chao Song, Wenjun Jiang, Xiaomin Wang, Ming Liu662-669
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show the superiority of TRAR compared with other state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, University of Electronic Science and Technology of China 2College of Computer Science and Electronic Engineering, Hunan University 3CETC Big Data Research Institute Co.,Ltd, Guiyang 4Big Data Application on Improving Government Governance Capabilities National Engineering Laboratory, Guiyang |
| Pseudocode | No | The paper describes algorithms but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | Another one is Foursquare dataset (Zhang et al. 2015), which is a publicly large-scale check-in LBSNs dataset. |
| Dataset Splits | Yes | We select the earliest 80% trips to train the model, and use the other 20% as a testing dataset. |
| Hardware Specification | Yes | We performed the evaluation of TRAR on a 3.3GHz CPU with 8GB of RAM. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | η1 and η2 in AR discovery are set at 800 and 0.3 as the default value. |