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DYNUCB: Dynamic Upper Confidence Bound

INTRODUCTION:

  • This is an implementation in java for our paper "Dynamic Clustering of Contextual Multi-Armed Bandits" (Nguyen & Lauw, CIKM 2014)

The folder includes:

  • Data: for importing the Delicious and LastFM database (using MYSQL(usr: 'root', pw: '') for connection).
  • Input: the norm matrix after reducing dimensions.
  • Output: the results is separated into N-tries (N threads) for taking the average.

*** Run from terminal:

java -Xmx4G -jar cikm.jar [args]

with arguments in order below:

  • 1st: algorithm selection (1-LinUCBSIN; 2-LinUCBIND; 3-DynUCB)
  • 2nd: dataset selection (1-delicious; 2-lastfm)
  • 3rd: the number of running times/threads of the selected algorithm (e.g. 10)
  • 4th: the number of iterations (e.g. 50000)
  • 5th(optional): the number of clusters (default 16-clusters) in case of choosing DynUCB algorithm

Examples

  • Running DynUCB algorithm on Delicious dataset with 16 clusters, 10 threads, 50000 iterations.\
java -Xmx4G -jar cikm.jar 3 1 10 50000 16

HOW TO CITE:

If you use DynUCB in your research, please cite the paper with the bibtex format below:

@inproceedings{
    nguyen2014dynamic,
    title={Dynamic clustering of contextual multi-armed bandits},
    author={Nguyen, Trong T and Lauw, Hady W},
    booktitle={Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management},
    pages={1959--1962},
    year={2014},
    organization={ACM}
}

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[CIKM'14] - A recommendation method based on multi-armed bandit framework with clustering

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