EEDN (SIGIR'23) [/paper]

The paper can be found in [/paper] or [ACM SIGIR](https://dl.acm.org/doi/10.1145/3539618.3591678).

Run

python Main.py

Note

Configures are given by Constants.py and Main.py
As mentioned in the paper, EEDN requires a shallow and wide architecture, please DO NOT over limit the embedding size for comparisons, unless there are not enough GPU memories.
When you apply EEDN on other datasets, as $\lambda$ and $\delta$ are sensitive, please tune these two hyperparameters by Optuna at least 100 times, which HAS BEEN IMPLEMENTED by the given code in Main.py (Line.160)
If you have any problem, please feel free to contact me at kaysenn@163.com.

Dependencies

Python 3.7.6
PyTorch version 1.7.1.

Datasets

Three files are required: train.txt (for training), tune.txt (for tuning), and test.txt (for testing).
Each line denotes an interaction including a user visited a POI at times.
The format is [#USER_ID]\t[#POI_ID]\t[#TIMES]\n, which is the same for all files.
For example,
0	0	1
0	1	3
0	3	2
1	2	1
the user (ID=0) visited the POI (ID=0) at 1 time, 
			  the POI (ID=1) at 3 times, 
			  and the POI (ID=3) at 2 times.
the user (ID=1) visited the POI (ID=2) at 1 time.

<table> <tr> <td> Dataset</td> <td> #Users</td> <td> #Items</td> <td> lambda</td> <td> delta </td> </tr> <tr> <td> Douban-book</td> <td> 12,859</td> <td> 22,294</td> <td> 0.5</td> <td> 1 </td> </tr> <tr> <td> Gowalla</td> <td> 18,737</td> <td> 32,510</td> <td> 1.5 </td> <td> 4 </td> </tr> <tr> <td> Foursquare</td> <td> 7,642</td> <td> 28,483</td> <td> 0.4</td> <td> 0.7 </td></tr> <tr> <td> Yelp challenge round 7</td> <td> 30,887</td> <td> 18,995</td> <td> 1</td> <td> 2.4 </td></tr> <tr> <td> Yelp2018</td> <td> 31,668</td> <td> 38,048</td> <td> 1</td> <td> 4 </td></tr> </table>

Baselines

SimGCL SIGIR'2022
NCL WWW'2022
DirectAU KDD'2022
STaTRL APIN'2022
SGL SIGIR'2021
SEPT KDD'2021
LightGCN SIGIR'2020
CPIR IJCAI'2020
ENMF TOIS'2020
SAE-NAD CIKM'2018

Citation

If this repository helps you, please cite:

@inproceedings{wang2023eedn,
  title={EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation},
  author={Wang, Xinfeng and Fukumoto, Fumiyo and Cui, Jin and Suzuki, Yoshimi and Li, Jiyi and Yu, Dongjin},
  booktitle={Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={383--392},
  year={2023}
}

Acknowledge

Thanks to Coder-Yu who collected many available baselines, and kindly released them.

EEDN

Install / Use

README