ssm-dnn
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Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction
Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction
This project is the implementation of proposal method in the paper
Files
-
./wide_deep.py
: Traditional official Wide&Deep -
./wide_deep_emb_conv.py
: Wide&Deep with convolution and pooling (SSM main code) -
./sparse_array_categorical_column.py
: Sparse array feature with CSV format input -
./submit_local.sh
: Run local
How to use
Execute sh submit_local.sh
to submit the job
type=wide_deep_conv # wide, deep, wide_deep, wide_deep_conv are available
python/bin/python3 wide_deep_emb_conv.py \
--checkpoints_dir=$model_dir \
--save_checkpoints_steps=1000 \
--batch_size=10000 \
--num_epochs=10000000 \
--data_dir=$sample_data \
--validate_dir=$eval_data \
--shuffle_buffer_size=10000 \
--embedding_model=./model_zoo/wide_deep_emb_conv_cluster/model.ckpt-114471 \
--pretrain=no \
--model_type=${type}
Arguments
-
--pretrain
- Indicates pretrain model or not.
-
--embedding_model
- The tf model file pre-trained.
Model Structure
- How SSM interect with Wide&Deep.
- How to construct convolution sequences.
- How "Delay Convolution" works, and it performs better than traditional conv-pool-conv-pool methods cause brings feature relations(convolutions) between different scale(poolings), and more powerful and efficient.
- What is the SSM output vector looks like, we use t-sne to mapping high-dimension into 2-d graph to figure out what SSM learned from those feature embeddings.