Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

Implementation of Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction using tensorflow

Prerequisites

Python 2.x
Tensorflow 1.4

Data

Getting Started

First we need to prepare data.

Amazon Prepare

You can get the raw Amazon data prepared

sh prepare_amazon.sh

Because getting and processing the data is time consuming，we had processed Amazon data and upload it for you.

sh prepare_ready_data.sh

Taobao Prepare

First download Taobao Data to get "UserBehavior.csv.zip", then execute the following command.

sh prepare_taobao.sh

Running

usage: train_book.py|train_taobao.py  [-h] [-p TRAIN|TEST] [--random_seed RANDOM_SEED]
                     [--model_type MODEL_TYPE] [--memory_size MEMORY_SIZE]
                     [--mem_induction MEM_INDUCTION]
                     [--util_reg UTIL_REG]

Base Model

The example for DNN

python script/train_book.py -p train --random_seed 19 --model_type DNN
python script/train_book.py -p test --random_seed 19 --model_type DNN

The model below had been supported:

DNN
PNN
DIN
GRU4REC
ARNN
RUM
DIEN
DIEN_with_neg

MIMN

You can train MIMN with different parameter setting:

MIMN Basic

python script/train_taobao.py -p train --random_seed 19 --model_type MIMN --memory_size 4 --mem_induction 0 --util_reg 0

MIMN with Memory Utilization Regularization

python script/train_taobao.py -p train --random_seed 19 --model_type MIMN --memory_size 4 --mem_induction 0 --util_reg 1

MIMN with Memory Utilization Regularization and Memory Induction Unit

python script/train_taobao.py -p train --random_seed 19 --model_type MIMN --memory_size 4 --mem_induction 1 --util_reg 1

MIMN with Auxiliary Loss

python script/train_taobao.py -p train --random_seed 19 --model_type MIMN_with_neg --memory_size 4 --mem_induction 0 --util_reg 0

If you want to train Amazon Data, you just need replace above train_taobao.py to train_book.py

JoannaPang/MIMN