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Code for "Dynamic Explainable Recommendation based on Neural Attentive Models"
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conf
data/der_data
img
model
results
.gitattributes
.gitignore
README.md
data_loader.py
evaluate.py
main.py
solver.py

README.md

Requirements

  • Python 2.7
  • TensorFlow 1.8
  • numpy 1.14

Project Structure

.
├── conf                    # Config files
├── data
    ├── der_data            # Experiment data   
├── results                 # Results saving
├── model          
    ├── DER.py              # The graph of DER
├── data_loader.py          
├── evaluate.py             # Evaluation code
├── main.py                 # The entrance of the project
└── solver.py               # The solver of the project     

Config

example:

[path]

root_path = your-local-path/DER

input_data_type = data/der_data/category

output_path = results

log_conf_path = conf/logging.conf

[parameters]

global_dimension = 8

word_dimension = 64

batch_size = 50

epoch = 50

learning_rate = 0.001

reg = 1

mode = validation

merge = FM

concat = 1

item_review_combine = add

item_review_combine_c = 0.5

lmd = 1

drop_out_rate = 0.7

Usage

  1. Install all the required packages

  2. process the raw data into the formats according to data/der_data/data_format

  3. Run python main.py

Author# DER

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