KANN

This repository is the implementation of The Knowledge-aware Attentional Neural Network for Movie Recommendation (KANN).

KANN is proposed to predict ratings and provide knowledge-level explanations by capturing understandable interactions between users and items at the knowledge level.

Environments

• python 3.8.11
• tensorflow 2.6
• numpy
• scipy

Files in the folder

• data/

  • amazon/: the dataset of Amazon movies & TV;
    • amazon.csv: the raw data of amazon dataset;
    • entity_emb50_concat_mean.bin: the context knowledge embeddings of amazon review entities;
    • entity_oriemb50.bin: the knowledge embeddings of amazon review entities;
    • entity_index: the indices of review entities.
    • 10core/: the preprocessed dataset with 10-core;
      • amazon.csv: the raw data of amazon's 10core dataset.
      • amazon.para: the parameters of the dataset (eg., the number of users/items);
      • amazon.test: randomly selected test dataset;
      • amazon.train: randomly selected train dataset;
      • amazon.valid: randomly selected valid dataset;
      • amazon_test.csv: raw test rating dataset;
      • amazon_train.csv: raw train rating dataset;
      • amazon_valid.csv: raw valid rating dataset;
      • entity_index: the indecies of review entities;
      • item_review: the dict of item reviews;
      • item_rid: item dict, key:item, value:users (who interact with the item);
      • user_review: the dict of user reviews;
      • user_rid: user dict, key:user, value:items (those reviewed by the user);
  • imdb/: the dataset of IMDb dataset;
    • imdb.csv: the raw data of imdb dataset;
    • entity_emb50_concat_mean.bin: the contex knowledge embeddings of imdb review entities;
    • entity_oriemb50.bin: the knowledge embeddings of imdb review entities;
    • 10core/: the preprocessed dataset with 10-core.

• output/

  • 10core/
    • checkpoints/: the trained models;
    • new_2layers_50d_4heads_1024dff_128batch_size/: the name is the hyper-parameters of KANN;
      • ckpt, etc.: the saving models of last 5 epoches;
    • logs/new_2layers_50d_4heads_1024dff_128batch_size: the saving results of KANN by recording into tensorboard.

• src

  • CustomSchedule.py: the customed parameter of attention mechanism;
  • Interacter.py: the encoding based on InteracterLayer;
  • InteracterLayer.py: the interaction layer for implementing inner and outer-attention mechanisms;
  • KANN.py: the implementation of KANN;
  • KANN_main.py: the main implementation by using one GPU;
  • KANN_multi_main.py: the main implementation by using multiple GPUs based on tf.distribute;
  • MultiHeadAttention.py: KANN's multi-head parallel processing;
  • build_dataset.py: the second step of preprocessing the raw dataset;
  • read_ratings.py: the first step of preprocessing the raw dataset.

The description of datasets

• amazon.csv
  • user_id: user ids.
  • item_id: item ids.
  • ratings: ratings from users to items.
  • reviews: ids of review entities.
• imdb.csv
  • user_id: user ids.
  • item_id: item ids.
  • ratings: ratings from users to items.
  • reviews: ids of review entities.

Running the code

For one GPU:

• cd src
• python read_rating.py
• python build_dataset.py
• python KANN_main.py

For multiple GPUs:

• cd src
• python read_rating.py
• python build_dataset.py
• python KANN_multi_main.py

Reference

if you find the code or data is useful, please cite our work:

@article{liu2022knowledge,
  title={Knowledge-aware attentional neural network for review-based movie recommendation with explanations},
  author={Liu, Yun and Miyazaki, Jun},
  journal={Neural Computing and Applications},
  pages={1--19},
  year={2022},
  publisher={Springer}
}