SESREC SIGIR 2023 Download - SESREC SIGIR 2023 Source code download

SESREC SIGIR 2023

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When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation

This is the official implementation of the SIGIR 2023 paper "When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation" based on PyTorch.

[arXiv] [ACM Digital Library]

News:

[2023.06.29] Recently, we released the first public dataset containing users' real search and recommendation behaviors to facilitate the potential research! You can find the dataset from https://kuaisar.github.io/.

Overview

The main implementation of SESRec can be found in the file models/SESRec.py. The architecture of SESRec is shown in the following figure:

Research Questions

We have concluded some frequently asked questions in the file FAQ.md.

Reproduction

Check the following instructions for reproducing experiments.

Experimental Setting

All the hyper-parameter settings of SESRec on both datasets can be found in files config/SESRec_commercial.yaml and config/SESRec_amazon.yaml. The settings of two datasets can be found in file config/const.py.

Dataset

Since the Kuaishou dataset is a proprietary industrial dataset, here we release the ready-to-use data of the Amazon (Kindle Store) dataset. The ready-to-use data can be downloaded from link.

Quick Start

1. Download data

Download and unzip data from this link. Place data files in the folder data.

2. Satisfy the requirements

Our experiments were done with the following python packages:

python==3.8.13
torch==1.9.0
numpy==1.23.2
pandas==1.4.4
scikit-learn==1.1.2
tqdm==4.64.0
PyYAML==6.0

3. Train and evaluate our model:

Run codes in command line:

python3 main.py --name SESRec --workspace ./workspace/SESRec --gpu_id 0  --epochs 30 --model SESRec  --batch_size 256 --dataset_name amazon

4. Check training and evaluation process:

After training, check log files, for example, workspace/SESRec/log/default.log.

Environments

We conducted the experiments based on the following environments:

CUDA Version: 11.1
OS: CentOS Linux release 7.4.1708 (Core)
GPU: The NVIDIA® T4 GPU
CPU: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz

Citation

Please cite our paper if you use this repository.

@inproceedings{si2023SESRec,
author = {Si, Zihua and Sun, Zhongxiang and Zhang, Xiao and Xu, Jun and Zang, Xiaoxue and Song, Yang and Gai, Kun and Wen, Ji-Rong},
title = {When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation},
year = {2023},
isbn = {9781450394086},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3539618.3591786},
doi = {10.1145/3539618.3591786},
booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {1313–1323},
numpages = {11},
keywords = {search, contrastive learning, disentanglement learning, recommendation},
location = {Taipei, Taiwan},
series = {SIGIR '23}
}