If you use the codes of this repository, please kindly cite following paper:

@article{ban2021convolutional,
  title={Convolutional Neural Bandit for Visual-aware Recommendation},
  author={Ban, Yikun and He, Jingrui},
  journal={arXiv preprint arXiv:2107.07438},
  year={2021}
}

CNN-UCB

Prerequisites:

python 3.8.8

CUDA 11.2

torch 1.9.0

torchvision 0.10.0

sklearn 0.24.1

numpy 1.20.1

scipy 1.6.2

pandas 1.2.4

Methods:

• cnn_ucb.py - Proposed algorithm CNN-UCB

• cnn-ts.py - Combine CNN with Thompson Sampling [Wang et al. 2021]

• cnn-epsilon.py - CNN with epsilon-greedy exploration strategy

• neural-epsilon.py - fully-connected neural network with epsilon-greedy exploration strategy

• neuralTS.py - Neural thompson sampling [Zhang et al. 2020]

• neuralUCB.py - Neural UCB [Zhou et al. 2020]

• kernalUCB.py - Kernal UCB [Valko et al., 2013a]

• linUCB.py - LinUCB [Li et al., 2010]

• packages.py - all the needed packages

• load_data.py - load the datasets

• cnn_class.py - the CNN classes

• process_yelp.ipynb - extract features from Yelp dataset

Datasets:

"mnist", "notmnist", "cifar10", "yelp"

Run:

python "method" --dataset "dataset"

For example, python cnn_ucb.py --dataset cifar10 ; python neuralUCB.py --dataset mnist

Results and configurations are in results.pdf

note:

To run dataset "notmnist", it is needed to download "imagedat.npy" and "labeldata.npy", and place them in the local folder.

To run dataset "yelp", it is needed to download " yelp_10000items_2000users_features.npy, yelp_2000users_10000items.npy.zip, yelp_2000users_10000items_features.npy" and unzip "yelp_2000users_10000items.npy.zip" and place them in the local folder.

Output: (number of round: regret, regret/number of round, loss)