If you use this code, please cite the following paper:

• Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Li Li, Zhaojie Liu, Yanlong Du. 2019. Click-Through Rate Prediction with the User Memory Network. In DLP-KDD. ACM.

1.3.0

ft - feature, slot == field

Data is in the "csv" format, where each row contains an instance.
Assume there are N unique fts. Fts need to be indexed from 1 to N. Use 0 for missing values or for padding.

We categorize fts as i) one-hot or univalent (e.g., user id, city) and ii) mul-hot or multivalent (e.g., words in ad title).

csv data format

• <label><one-hot fts><mul-hot fts>

We also need to define the max number of features per mul-hot ft slot (through the "max_len_per_slot" parameter) and perform trimming or padding accordingly. Please refer to the following example for more detail.

1. original fts (ft_name:ft_value)

• label:0, gender:male, age:27, query:apple, title:apple, title:fruit, title:fresh
• label:1, gender:female, age:35, query:shoes, query:winter, title:shoes, title:winter, title:warm, title:sales

2. csv fts (not converted to ft index yet)

• 0, male, 27, apple, 0, 0, apple, fruit, fresh
• 1, female, 35, shoes, winter, 0, shoes, winter, warm

csv format settings:
n_one_hot_slot = 2 # num of one-hot ft slots (gender, age)
n_mul_hot_slot = 2 # num of mul-hot ft slots (query, title)
max_len_per_slot = 3 # max num of fts per mul-hot ft slot

For the first instance, the mul-hot ft slot "query" contains only 1 ft "apple". We thus pad (max_len_per_slot - 1) zeros, resulting in "apple, 0, 0".
For the second instance, the mul-hot ft slot "title" contains 4 fts. We thus only keep the first max_len_per_slot fts.

• config.py -- config file
• ctr_funcs.py -- functions
• model_dnn.py -- DNN model for click-through rate prediction
• model_mem.py -- Deep memory network model for click-through rate prediction
• model_wide_deep.py -- Wide & Deep model for click-through rate prediction
• model_mem_wide_deep.py -- Wide & Deep model with user memory network unit for click-through rate prediction
• train_mem.py -- Training script for deep memory network, and you can train other models by modifing this script

First revise the config file, and then run the code

nohup python train_mem.py > [output_file_name] 2>&1 &