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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ ---
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+
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+ # LLM4POI
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+
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+ ## Dataset Summary
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+
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+ A preprocessed version of [LLM4POI](https://github.com/neolifer/LLM4POI), including the FourSquare-NYC, Gowalla-CA, and FourSquare-TKY datasets. Please refer to their repository for more details.
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+
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+ LLM4POI frames next POI prediction task into a question-answering problem that is fed as prompt into a large language model (LLM). The model is trained to generate the next POI given the current trajectory and the historical trajectory. This repository hosts both the Q&A version and the raw txt and csv versions of the datasets.
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+
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+ This dataset is used to train GenUP models as described in our paper [GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems](https://arxiv.org/abs/2410.20643).
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+
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+ ### Dataset Sources
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+
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+ Repository: [neolifer/LLM4POI](https://github.com/neolifer/LLM4POI)
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+
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+ Paper: [Large Language Models for Next Point-of-Interest Recommendation](https://arxiv.org/abs/2404.17591)
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+
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+
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+ Repository: [w11wo/GenUP](https://github.com/w11wo/GenUP)
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+ Paper: [GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems](https://arxiv.org/abs/2410.20643)
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+
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+ ## Dataset Structure
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+
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+ ```shell
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+ .
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+ β”œβ”€β”€ README.md
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+ β”œβ”€β”€ ca
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+ β”‚Β Β  └── preprocessed
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+ β”‚Β Β  β”œβ”€β”€ test_qa_pairs_kqt.txt
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+ β”‚Β Β  β”œβ”€β”€ train_qa_pairs_kqt.json
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+ β”‚Β Β  └── train_sample.csv
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+ β”œβ”€β”€ nyc
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+ β”‚Β Β  └── preprocessed
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+ β”‚Β Β  β”œβ”€β”€ test_qa_pairs_kqt.txt
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+ β”‚Β Β  β”œβ”€β”€ train_qa_pairs_kqt.json
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+ β”‚Β Β  └── train_sample.csv
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+ └── tky
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+ └── preprocessed
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+ β”œβ”€β”€ test_qa_pairs_kqt.txt
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+ β”œβ”€β”€ train_qa_pairs_kqt.json
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+ └── train_sample.csv
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+ ```
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+
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+ ### Data Instances
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+ An example of a line in `test_qa_pairs_kqt.txt`:
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+
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+ ```plaintext
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+ <question>: The following data is a trajectory of user 2: At 2010-09-25 01:38:14, user 2 visited POI id 247 which is a Stadium and has Category id 261. At 2010-09-25 02:11:34, ... Given the data, At 2010-09-25 20:34:27, Which POI id will user 2 visit? Note that POI id is an integer in the range from 0 to 9690.<answer>: At 2010-09-25 20:34:27, user 2 will visit POI id 6350.
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+ ```
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+
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+ An example of a JSON object in `train_qa_pairs_kqt.json`:
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+
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+ ```json
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+ {
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+ "question": "The following data is a trajectory of user 2: At 2010-09-25 01:38:14, user 2 visited POI id 247 which is a Stadium and has Category id 261. At 2010-09-25 02:11:34, ... Given the data, At 2010-09-25 20:34:27, Which POI id will user 2 visit? Note that POI id is an integer in the range from 0 to 9690.",
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+ "answer": "At 2010-09-25 20:34:27, user 2 will visit POI id 6350."
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+ }
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+ ```
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+
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+ An example of entries in `train_sample.csv`:
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+
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+ ```csv
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+ check_ins_id,UTCTimeOffset,UTCTimeOffsetEpoch,pseudo_session_trajectory_id,UserId,Latitude,Longitude,PoiId,PoiCategoryId,PoiCategoryName
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+ 126094.0,2010-06-06 18:48:32,1275814112,0,1,37.6163560649,-122.3861503601,445,207,Airport
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+ 126278.0,2010-06-06 22:11:04,1275826264,0,1,37.7826046833,-122.4076080167,244,1,Coffee Shop
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+ 126314.0,2010-06-06 22:40:29,1275828029,0,1,37.7831295924,-122.4038743973,9346,121,Conference
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+ 126622.0,2010-06-07 06:01:04,1275854464,0,1,37.7815086,-122.4050282333,3253,128,Pub
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+ ```
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+
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+ ### Data Splits
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+ | | train | test |
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+ | --- | ----: | ---: |
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+ | NYC | 11022 | 1447 |
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+ | CA | 36374 | 2864 |
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+ | TKY | 51661 | 7079 |
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+
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+ ## Additional Information
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+
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+ ### Citation
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+
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+ If you find this repository useful for your research, please consider citing our paper:
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+
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+ ```bibtex
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+ @misc{wongso2024genupgenerativeuserprofilers,
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+ title={GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems},
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+ author={Wilson Wongso and Hao Xue and Flora D. Salim},
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+ year={2024},
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+ eprint={2410.20643},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.IR},
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+ url={https://arxiv.org/abs/2410.20643},
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+ }
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+ ```
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+
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+ ```bibtex
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+ @inproceedings{Li_2024, series={SIGIR 2024},
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+ title={Large Language Models for Next Point-of-Interest Recommendation},
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+ url={http://dx.doi.org/10.1145/3626772.3657840},
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+ DOI={10.1145/3626772.3657840},
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+ booktitle={Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
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+ publisher={ACM},
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+ author={Li, Peibo and de Rijke, Maarten and Xue, Hao and Ao, Shuang and Song, Yang and Salim, Flora D.},
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+ year={2024},
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+ month=jul, pages={1463–1472},
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+ collection={SIGIR 2024}
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+ }
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+ ```
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+
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+ ### Contact
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+
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+ If you have any questions or suggestions, feel free to contact Wilson at `w.wongso(at)unsw(dot)edu(dot)au`.