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].
Location Predicts You: Location Prediction via Bi-direction Speculation and Dual-level Association
Authors: Xixi Li, Ruimin Hu, Zheng Wang, Toshihiko Yamasaki
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two public datasets demonstrate that BSDA achieves significant improvements over state-of-the-art methods. |
| Researcher Affiliation | Academia | 1National Engineering Research Center for Multimedia Software (NERCMS), School of Computer Science, Wuhan University 2Research Institute for an Inclusive Society through Engineering (RIISE), The University of Tokyo 3Department of Information and Communication Engineering, The University of Tokyo EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methodology in text and with a diagram, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing its source code or links to a code repository. |
| Open Datasets | Yes | We conduct experiments on two widely-used real-world check-in datasets: Gowalla [Cho et al., 2011] and Foursquare [Yang et al., 2014]. |
| Dataset Splits | No | Following the setting in [Yang et al., 2020], we eliminate inactive users who have records less than 100. We take the first 80% check-ins as the training set, the other 20% as the testing set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions types of neural networks used (RNN, LSTM, GRU) but does not provide specific software dependencies or library version numbers. |
| Experiment Setup | Yes | We empirically set the dimension of hidden states for RNN units and the dimension of embeddings as 10. Parameters in flashback block including temporal decay α and spatial decay β follows the setting in [Yang et al., 2020]. |