README.md

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.

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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.

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

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.