SLRC
Short-term and Life-time Repeat Consumption (SLRC)
Model repeat consuming behavior with combination of Collaborative Filtering (CF) and Hawkes Process.
This is our implementation for the paper:
Chenyang Wang, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2019. Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems. In WWW'19.
Three models are implemented when integrating different CF methods:
- SLRC_BPR: Bayesian Personalized Ranking (BPR) is a matrix factorization model with pairwise loss.
- SLRC_Tensor: Tensor incorporates time factor with tensor factorization.
- SLRC_NCF: Neural Collaborative Filtering (NCF) utilize nerual network to solve CF problem.
Please cite our paper if you use our codes. Thanks!
@inproceedings{wang2019modeling,
title={Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems},
author={Wang, Chenyang and Zhang, Min and Ma, Weizhi and Liu, Yiqun and Ma, Shaoping},
booktitle={The World Wide Web Conference},
pages={1977--1987},
year={2019},
organization={ACM}
}
Author: Chenyang Wang ([email protected])
Environments
python 3.5.2
tensorflow 1.2.0
pandas 0.23.4
numpy 1.16.0
sklearn 0.20.2
tqdm 4.29.1
If you want to train with GPU, tensorflow-gpu==1.2.0 is required.
Datasets
- Baby: Online purchasing information within baby category from an e-commerce retailer. Not publicly available.
- Order: Mobile payment records in supermarkets and convenient stores. Not publicly available. But we have release anonymized data online.
- Recsys2017: Click log of job posting. The original data and description is avaliable here.
- BrightKite: User check-in data to physical locations. The original data and description is avaliable here.
The format description of origin data and dataset after preprocessing can be found in ./data/README.md.
Usage
Download original data and put it into ./data/ (or other data in the same format)
> cd SLRC/src
# make sure main.py can find necessary modules
> export PYTHONPATH=../
# generate dataset
> python Preprocess.py --dataset order
# SLRC_BPR in Order dataset
> python main.py --cf BPR --dataset order --gpu '' --K 100 --batch_size 256 --l2 1e-4 --lr 1e-4
According to our experiences, SLRC_BPR generally works well and takes less time to train, which is the most robust. SLRC_NCF relies on fine-tuned parameters to get good results (sometimes still worse than SLRC_BPR). Therefore, we recommend to use BPR as the Collaborative filtering (CF) method to calculate base intensity.
Example training log in Order dataset can be found in ./log/.
Last Update Date: Oct. 18, 2019