This project is a tensorflow implementation of DIN (Deep Interest Network for Click-Through Rate Prediction)

We abstract DIN into the following structure:

• vector: directly fed into the MLP, e.g., user profile
• sequence: sequential values that will be attentioned, e.g., history click sequence. There can be multiple sequences, e.g., purchasing history and browsing history.
• target: target value that will be directly fed into the MLP of DIN, and applies attention to the sequence inputs, e.g., target page

Each input value can come in two ways:

• value: continuous input values, e.g., time spent on a page
• embedding: discrete input values which will be passed into an embedding layer, e.g., page name

Therefore, we classify the model inputs into 6 categories: emb_vec, emb_seq, emb_tgt, val_vec, val_seq, val_tgt. We implement DIN based on such abstract, independent with specific model inputs. Pass a config dict to the model (example), and the model can construct the computation graph automatically.

1. Run data/generate_samples.py to generate demo samples:

   python data/generate_samples.py

2. Create config.py under scrips/ to specify your project absolute path, e.g.,

   project_fd = '/root/din/'

3. Run scripts/train_din.py to start the training process:

   python scripts/train_din.py

• If you have a customized dataset, modify common.py, specify:

  train_data_fp = "path/to/trainset/file"
  test_data_fp = "path/to/testset/file"

• If need a customized dataset with customized model structure, modify fea_config.py, and write a FEA_CONFIG dict and SHARED_EMB_CONFIG dict.

scripts
├── common.py: global configuration
├── config.py: global configuration
├── data: data reading module
│   ├── dataset.py: object to read, shuffle, and preprocess data
│   └── fea_config.py: configuration for preprocess and model construction
├── model
│   ├── din.py: DIN object
│   ├── layers
│   │   ├── activation.py: activation layers (PReLU and Dice)
│   │   ├── attention.py: attention layers
│   │   ├── forward_net.py: MLP layers
│   │   └── interface.py: abstract class for layers
│   └── utils
│       ├── constant.py: constants
│       ├── input_fn.py: wrapper for estimator training
│       ├── proc_input_config.py: utils function
│       └── widgets.py: utils function
├── train
│   ├── evaluate.py: evaluation code
│   ├── metrics.py: evaluation metrics
│   └── vanilla_train.py: training code
└── train_din.py: entry file

The Din class defined in din.py has the following functions:

• build_graph_: build computation graph, and store the input and output nodes as attributes
  • attributes:
    • features_ph, labels_ph: input nodes
    • outputs: output nodes
    • session, graph: computation graph
    • saver: checkpoint saver
  • relevant methods:
    • switch_graph: switch to your specified graph if multiple graph exists
    • load_from: load the variable values from a target graph
• model_fn: for estimator invocation
• freeze: set trainable=False for variables (invoke before build_graph please)