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STHGCN

This repository includes the implementation details of the method STHGCN and helps readers to reproduce the results in the paper Spatio-Temporal Hypergraph Learning for Next POI Recommendation, whose objective is to utilize hypergraph convolution networks to model diverse user behaviors in the next-POI recommendation task.

In particular, we introduce a hypergraph to construct the complex structure of check-ins and trajectories. We develop hypergraph transformer layers to capture high-order heterogeneous inter-user and intra-user trajectories correlations while incorporating spatio-temporal contexts. Comprehensive experiments conducted on three real-world datasets have demonstrated the superiority of STHGCN in the task of next-POI recommendation, outperforming baseline models by a large margin. For more information, please see our paper Spatio-Temporal Hypergraph Learning for Next POI Recommendation (Yan et al. 2023).

  • Multi-Level Hypergraph Multi-Level Hypergraph

  • Model Framework STHGCN Overall Framework

Installation

  1. Clone the repository (If showing error of no permission, need to first add a new SSH key to your GitHub account.): 
    git clone https://github.com/ant-research/Spatio-Temporal-Hypergraph-Model.git
  2. The repository has some important dependencies below, and we don't guarantee that the code still works if using higher versions. Please refer to the respective page to install:
    • Pytorch == 1.7.0
    • pytorch_geometric == 1.7.2
      • torch-scatter == 2.0.7
      • torch-sparse == 0.6.9
      • torch-cluster == 1.5.9
      • torch-spline-conv == 1.2.1
  3. Install other dependencies in requirements.txt: 
    pip install -r requirements.txt

Hardware

Here are the minimum requirements of the hardware including CPU and GPU.

  • CPU: core 16, memory 30GB
  • GPU: Tesla V100 (16GB)

Important messages you need to know:

  1. For Foursquare-TKY dataset, if the first value of hyperparameter sizes is more than 300 such as "400-240", please use 32GB GPU;
  2. If you use other type of GPU like Tesla P100, it may produce different results even if you use the best configuration file with the same random seed.

Dataset

The Foursquare-NYC dataset is collected in New York city and the Foursquare-TKY dataset is collected in Tokyo over 11 months (from April 2012 to February 2013) from Foursquare. The Gowalla-CA dataset with a longer time period (from March 2009 to October 2010) and broader geographical coverage are collected in California and Nevada on the Gowalla platform.

Preprocess

The preprocess step includes creating train/validate/test sample files and generating hypergraph-related PyG data files. The preprocess step will be excuted the first time we train our model using run.py, this step will be skipped if we already preprocess the same data before.

To get the raw data:

  • Unzip data/nyc/raw.zip to data/nyc. Then you will get raw directory with three .csv files, including trainginng sample file NYC_train.csv, validating sample file NYC_val.csv and testing sample file NYC_test.csv.
  • Unzip data/tky/raw.zip to data/tky. Then you will get raw directory with file dataset_TSMC2014_TKY.txt containing all the checkin information.
  • Unzip data/ca/raw.zip to data/ca. Then you will get raw directory with file loc-gowalla_totalCheckins.txt containing raw checkin records, gowalla_spots_subset1.csv containing category-involved checkin records, and us_state_polygon_json.json containing the POI polygon of every state in U.S. To get the raw data of CA used for preprocessing including the correct category information, run 
    python generate_ca_raw.py

If you want to compare your model with our work in the same preprocess setting, we strongly suggest to directly use the sample.csv, train_sample.csv, validate_sample.csv, and test_sample.csv sample files in the preprocessed directories.

Statistical Information

After preprocessing (some works only show the statistics before preprocessing), the key statistics of the three dtasets are shown below. The first 6 columns are calculated based on sample.csv which is all the samples before removing the unseen user or poi. Meanwhile, the last 3 columns are calculated based on train_sample.csv, validate_sample.csv and test_sample.csv.

Dataset Name #user #poi #category #check-in #trajectory #training sample #validation sample #testing sample
Foursquare-NYC 1,048 4,981 318 103,941 14,130 72,206 1,400 1,347
Foursquare-TKY 2,282 7,833 290 405,000 65,499 274,597 6,868 7,038
Gowalla-CA 3,957 9,690 296 238,369 45,123 154,253 3,529 2,780

Original Link

In case of some readers feel confused from the data provided by our work and by other works, here we introduce where and how our data comes from.

Actually, we suffered a lot from searching for the valid data from previous works. STAN provides the Raw data of NYC, TKY and CA, but these data lack category information. GETNext only provides the Preprocessed data of NYC, while TKY and CA are missing. We use the raw NYC data from STAN's link, and preprocess them based on the description from GETNext. Unfortunately, there still be a minor gap between our preprocessed NYC data with what is provided by GETNext.

For fair comparison of different models, for NYC, we download the preprocessed files from GETNext; For TKY and CA, we download them from the link provided by STAN, we also fetch the category information from IRenMF. We run GETNext model with our preprocessed data and the performances are only a little worse (~0.01 on Acc@1) than what are reported in thier paper, so we just use the performances reported in their paper.

Thanks for all the data providers.

Main Experimental Results

To reproduce the main results in our paper. Please follow the steps below.

Main Performance

To know the meaning of every config in yaml file, please refer to conf/README.md.

We can reproduce the best performance of our model with the script below. Please choose 'nyc', 'tky', or 'ca' for {dataset_name}.

python run.py -f best_conf/{dataset_name}.yml
Dataset Name Acc@1 Acc@5 Acc@10 MRR #Parameters Training Speed
(per epoch)
NYC 0.2734 0.5361 0.6244 0.3915 27,820,020 3m24s
TKY 0.2950 0.5207 0.5980 0.3986 30,167,576 59m31s
CA 0.1730 0.3529 0.4191 0.2558 31,810,778 15m40s

The average performances of 10 runs can be achived using the script below. -n denotes the total number of experiments. -g denotes the gpu id (default 0).

python multiple_run.py -f best_conf/{dataset_name}.yml -n 10 -g 0
Dataset Name Acc@1 Acc@5 Acc@10 MRR
NYC 0.2625 ± 0.0054 0.5226 ± 0.0033 0.6117 ± 0.0044 0.3798 ± 0.0041
TKY 0.2905 ± 0.0035 0.5184 ± 0.0035 0.5969 ± 0.0030 0.3951 ± 0.0025
CA 0.1652 ± 0.0036 0.3405 ± 0.0041 0.4177 ± 0.0038 0.2491 ± 0.0026

Ablation Study

Based on the configuration for best model in conf/best_conf/, we only modify some key configs to do ablation study.

  • For w/o Hypergraph, we set model_name: seq_transformer. It's worth to mension that we use the same NeighborSampler and transform the adjacency list into sequential input for Transformer model. So the inputs are the same with vanilla Transformer. Please run
python multiple_run.py -f ablation_conf/{dataset_name}_wo_hypergraph.yml -n 10 -g 0
  • For w/o ST Information, we set time_fusion_mode: . Please run
python multiple_run.py -f ablation_conf/{dataset_name}_wo_st_info.yml -n 10 -g 0
  • For w/o Hyperedge Collaboration, we set do_traj2traj: False
python multiple_run.py -f ablation_conf/{dataset_name}_wo_hyper_collab.yml -n 10 -g 0

The ablation results are list below, which are consistent though slightly different with Table 4 in our paper:

  • NYC:
Acc@1 Acc@5 Acc@10 MRR
Full Model 0.2625 ± 0.0054 0.5226 ± 0.0033 0.6117 ± 0.0044 0.3798 ± 0.0041
w/o Hypergraph 0.2391 ± 0.0068 0.5137 ± 0.0094 0.6069 ± 0.0091 0.3618 ± 0.0050
w/o ST Information 0.2332 ± 0.0048 0.5113 ± 0.0039 0.6091 ± 0.0069 0.3591 ± 0.0034
w/o Hyperedge Collaboration 0.2490 ± 0.0058 0.5048 ± 0.0098 0.5885 ± 0.0052 0.3641 ± 0.0055
  • TKY:
Acc@1 Acc@5 Acc@10 MRR
Full Model 0.2905 ± 0.0035 0.5184 ± 0.0035 0.5969 ± 0.0030 0.3951 ± 0.0025
w/o Hypergraph 0.2368 ± 0.0018 0.4453 ± 0.0016 0.5222 ± 0.0015 0.3337 ± 0.0014
w/o ST Information 0.2629 ± 0.0051 0.4941 ± 0.0039 0.5770 ± 0.0030 0.3689 ± 0.0037
w/o Hyperedge Collaboration 0.2455 ± 0.0027 0.4589 ± 0.0031 0.5361 ± 0.0026 0.3446 ± 0.0018
  • CA:
Acc@1 Acc@5 Acc@10 MRR
Full Model 0.1652 ± 0.0036 0.3405 ± 0.0041 0.4177 ± 0.0038 0.2491 ± 0.0026
w/o Hypergraph 0.1476 ± 0.0031 0.3146 ± 0.0024 0.3859 ± 0.0051 0.2270 ± 0.0028
w/o ST Information 0.1578 ± 0.0042 0.3242 ± 0.0037 0.4021 ± 0.0054 0.2384 ± 0.0032
w/o Hyperedge Collaboration 0.1538 ± 0.0028 0.3227 ± 0.0046 0.3917 ± 0.0045 0.2341 ± 0.0028

Tensorboard

All the measurements and visualizations can be displayed via tensorboard tool. The tensorboard files are in tensorboard directory.

tensorboard --logdir {tensorboard_directory}

If you want to analysis the experimental results such as measuring the mean and standard deviation of 10 runs of the same hyper-parameter setting, you can just download the TABLE VIEW data as csv files under HPARAMS tab.

Citation

If you compare with, build on, or use aspects of the STHGCN, please cite the following:

  @inproceedings{sigir2023sthgcn,
  title={Spatio-Temporal Hypergraph Learning for Next POI Recommendation},
  author={Yan, Xiaodong, and Song, Tengwei and Jiao, Yifeng and He, Jianshan and Wang, Jiaotuan and Li, Ruopeng and Chu, Wei},
  booktitle={Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2023},
  series={SIGIR '23}
  }