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LLM4POI

Dataset Summary

A preprocessed version of LLM4POI, including the FourSquare-NYC, Gowalla-CA, and FourSquare-TKY datasets. Please refer to their repository for more details.

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.

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.

Dataset Sources

Repository: neolifer/LLM4POI

Paper: Large Language Models for Next Point-of-Interest Recommendation

Repository: w11wo/GenUP

Paper: GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems

Dataset Structure

.
β”œβ”€β”€ README.md
β”œβ”€β”€ ca
β”‚   └── preprocessed
β”‚       β”œβ”€β”€ test_qa_pairs_kqt.txt
β”‚       β”œβ”€β”€ train_qa_pairs_kqt.json
β”‚       └── train_sample.csv
β”œβ”€β”€ nyc
β”‚   └── preprocessed
β”‚       β”œβ”€β”€ test_qa_pairs_kqt.txt
β”‚       β”œβ”€β”€ train_qa_pairs_kqt.json
β”‚       └── train_sample.csv
└── tky
    └── preprocessed
        β”œβ”€β”€ test_qa_pairs_kqt.txt
        β”œβ”€β”€ train_qa_pairs_kqt.json
        └── train_sample.csv

Data Instances

An example of a line in test_qa_pairs_kqt.txt:

<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.

An example of a JSON object in train_qa_pairs_kqt.json:

{
  "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."
}

An example of entries in train_sample.csv:

check_ins_id,UTCTimeOffset,UTCTimeOffsetEpoch,pseudo_session_trajectory_id,UserId,Latitude,Longitude,PoiId,PoiCategoryId,PoiCategoryName
126094.0,2010-06-06 18:48:32,1275814112,0,1,37.6163560649,-122.3861503601,445,207,Airport
126278.0,2010-06-06 22:11:04,1275826264,0,1,37.7826046833,-122.4076080167,244,1,Coffee Shop
126314.0,2010-06-06 22:40:29,1275828029,0,1,37.7831295924,-122.4038743973,9346,121,Conference
126622.0,2010-06-07 06:01:04,1275854464,0,1,37.7815086,-122.4050282333,3253,128,Pub

Data Splits

train test
NYC 11022 1447
CA 36374 2864
TKY 51661 7079

Additional Information

Citation

If you find this repository useful for your research, please consider citing our paper:

@misc{wongso2024genupgenerativeuserprofilers,
  title={GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems}, 
  author={Wilson Wongso and Hao Xue and Flora D. Salim},
  year={2024},
  eprint={2410.20643},
  archivePrefix={arXiv},
  primaryClass={cs.IR},
  url={https://arxiv.org/abs/2410.20643}, 
}
@inproceedings{Li_2024, series={SIGIR 2024},
  title={Large Language Models for Next Point-of-Interest Recommendation},
  url={http://dx.doi.org/10.1145/3626772.3657840},
  DOI={10.1145/3626772.3657840},
  booktitle={Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  publisher={ACM},
  author={Li, Peibo and de Rijke, Maarten and Xue, Hao and Ao, Shuang and Song, Yang and Salim, Flora D.},
  year={2024},
  month=jul, pages={1463–1472},
  collection={SIGIR 2024}
}

Contact

If you have any questions or suggestions, feel free to contact Wilson at w.wongso(at)unsw(dot)edu(dot)au.

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