NFARec (SIGIR'24)

Paper - [ArXiv]

• NFARec: A Negative Feedback-Aware Recommender Model, SIGIR 2024 Oral.
• Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Dongjin Yu.

Run

python Main.py

Note

• Configures are given by Constants.py and Main.py
• If you have any problem, please feel free to contact me at kaysenn@163.com.

Dependencies

pip install -r requirement.txt

---

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 rated an item with a score at times (or timestamp).
The format is [#USER_ID]\t[#ITEM_ID]\t[#SCORES]\t[#TIMES]\n, which is the same for all files.
For example,
0	0	5	1
0	1	4	3
0	3	1	2
1	2	4	1
the user (ID=0) rated the item (ID=0) with the score of 5 at 1 time (or timestamp), 
			  the item (ID=1) with the score of 4 at 3 times (or timestamp), 
			  and the item (ID=3) with the score of 1 at 2 times  (or timestamp).
the user (ID=1) rated the item (ID=2) with the score of 4 at 1 time (or timestamp).

Citation

If this repository helps you, please cite:

@inproceedings{wang2024nfarec,
  title={NFARec: A Negative Feedback-Aware Recommender Model},
  author={Wang, Xinfeng and Fukumoto, Fumiyo and Cui, Jin and Suzuki, Yoshimi and Yu, Dongjin},
  booktitle={Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={935–-945},
  year={2024}
}