The source code for our RecSys 2023 Paper "Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems"
We propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code will be released.
Our code is based on PyTorch 1.7.1 and runnable for both windows and ubuntu server. Required python packages:
- numpy
- torch
- faiss-gpu
Original links of datasets are:
http://jmcauley.ucsd.edu/data/amazon/index.html
https://tianchi.aliyun.com/dataset/dataDetail?dataId=649&userId=1
https://github.com/RUCAIBox/RecSysDatasets
You can run python process/data.py {dataset_name} to preprocess the datasets.
Make sure reviews_Books_5.json file is located in current directory
(Download the raw file at https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2
)
$ git clone https://github.com/Tokkiu/REMI.git
$ cd REMI
$ python process/data.py book
conda env create --file env.yml
Run the baseline.
$ python src/train.py --model_type ComiRec-SA --gpu 0 --dataset book
$ python src/train.py --model_type ComiRec-SA --gpu 0 --dataset gowalla
$ python src/train.py --model_type ComiRec-SA --gpu 0 --dataset rocket
Available baselines:
- ComiRec-SA
- ComiRec-DR
- MIND
- Pop
- GRU4Rec
- DNN
Reproduce the reported result.
$ python src/train.py --model_type REMI --gpu 0 --dataset book --rlambda 100 --rbeta 10
$ python src/train.py --model_type REMI --gpu 0 --dataset gowalla --rlambda 100 --rbeta 1
$ python src/train.py --model_type REMI --gpu 0 --dataset rocket --rlambda 100 --rbeta 0.1
The --rbeta is used to activate IHN module. (available in whole framework)
The --rlambda is used to activate RR module. (available in REMI)
If you find this repo useful, please cite
@inproceedings{xie2023rethinking,
title = {Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems},
author = {Yueqi Xie and Jingqi Gao and Peilin Zhou and Qichen Ye and Yining Hua and Jaeboum Kim and Fangzhao Wu and Sunghun Kim},
booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems},
year = {2023},
}
This repo is based on the following repositories:
Feel free to contact us if there is any question. (YueqiXIE, yxieay@connect.ust.hk; Jingqi Gao, mrgao.ary@gmail.com; Peilin Zhou, zhoupalin@gmail.com; Russell KIM, russellkim@upstage.ai)