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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Taming the Long Tail in Human Mobility Prediction
Authors: Xiaohang Xu, Renhe Jiang, Chuang Yang, zipei fan, Kaoru Sezaki
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with two real-world trajectory datasets demonstrate that Lo TNext significantly surpasses existing state-of-the-art works. and We evaluate our Lo TNext on two publicly available real-world LBSN datasets: Gowalla and Foursquare |
| Researcher Affiliation | Academia | Xiaohang Xu1, Renhe Jiang1 , Chuang Yang1, Zipei Fan1, Kaoru Sezaki1 1The University of Tokyo EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Pseudo-code of training Lo TNext |
| Open Source Code | Yes | 2https://github.com/Yukayo/Lo TNext |
| Open Datasets | Yes | We evaluate our Lo TNext on two publicly available real-world LBSN datasets: Gowalla and Foursquare1 2 |
| Dataset Splits | No | We then split each user s check-in records according to temporal order, using the first 80% for training and the remaining 20% for testing. The paper does not explicitly state a validation dataset split for purposes like hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | We implement Lo TNext using Py Torch 1.13.1 on a Linux server equipped with 384GB RAM, 10-core Intel(R) Xeon(R) Silver 4210R CPU @ 2.40GHz, and Nvidia RTX 3090 GPUs. |
| Software Dependencies | Yes | We implement Lo TNext using Py Torch 1.13.1 |
| Experiment Setup | Yes | The embedding dimensions for POIs and users are set to 10, and the time embedding dimension is set to 6. For the Transformer architecture, we incorporate two multi-head attention mechanisms and 2 encoder blocks. For the spatial decay rate β, we follow the settings of Flashback [43]. and The results, shown in Figure 6(a) for Gowalla and Figure 6(c) for Foursquare, indicate that Acc@1 and MRR remain stable across different values, with the optimal threshold identified as δ = 0.5. Next, we vary the logit adjustment weight τ from 1 to 2 in increments of 0.2 to test the model s performance in balancing class imbalances. Figure 6(b) and Figure 6(d) reveal that τ = 1.2 yields the best results on both datasets, suggesting a moderate adjustment weight helps generalize better without overly amplifying rare classes. |