king0980692 / torch_seq2rec

torch seq2rec

Data & Preprocess

diginetica

• Data Download

mkdir data/diginetica

1. download the dataset-train-diginetica.zip from link
2. unzip the file and put click-item-views.csv is under data/diginetica folder

• Preprocess
  1. remove items with item_counts<=4
  2. remove ssessions with session_length=1
  3. use last week as testing data

yoochoose(1/4)

• Data Download

mkdir data/yoochoose
cd data/yoochoose
wget https://s3-eu-west-1.amazonaws.com/yc-rdata/yoochoose-data.7z
7z x yoochoose-data.7z -o./yoochoose-data

• Preprocess
  1. remove items with item_counts<=4
  2. remove ssessions with session_length=1
  3. sort sessions by timestamp and only keep last 1/4 sessions
  4. use last day as testing data

GRU4REC

Implementation

pip install -r model/gru4rec/requirements.txt
cd scripts
./run_gru4rec_[dataset_name].sh

This script will do

1. preprocess the original data in data folder
2. save train.csv and test.csv in exp folder
3. train gru4rec model
4. evaluate the model with Recall & MRR & NDCG

SRGNN

Original code from here. Original paper.

preprocessing

python ./model/SRGNN/preprocess.py

Implementation

pip install -r ./model/SRGNN/requirement.txt 
./script/run_SRGNN_[dataset_name].sh 

sasRec

Paper

Wang-Cheng Kang, Julian McAuley (2018). Self-Attentive Sequential Recommendation. In Proceedings of IEEE International Conference on Data Mining (ICDM'18)

Implementation

cd script
./sasRec_yoochoose.sh

Preprocess

• Data Dowmload into data folder
• You need to run the file model/sasRec/DataProcess.py first to preprocess the data.
• data preprocess

 python3 ./model/sasRec/DataProcessing.py --dataset=yoochoose

Run model

-you can run the file main.py or to train the model. For example:

python3 main.py --dataset=sample

-You can change other parameters according to the usage: if your dataset is from yoochoose

python3 ./model/sasRec/main.py  --dataset=yoochoose