@inproceedings{4c308e147ce14474b1b0f9365b8200fb,
title = "Fast online learning to recommend a diverse set from big data",
abstract = "Building a recommendation system to withstand the rapid change in items{\textquoteright} relevance to users is a challenge requiring continual optimization. In a Big Data scenario, it becomes a harder problem, in which users get substantially diverse in their tastes. We propose an algorithm that is based on the UBC1 bandit algorithm to cover a large variety of users. To enhance UCB1, we designed a new rewarding scheme to encourage the bandits to choose items that satisfy a large number of users. Our approach takes account of the correlation among the items preferred by different types of users, in effect, increasing the coverage of the recommendation set efficiently. Our method performs better than existing techniques such as Ranked Bandits [8] and Independent Bandits [6] in terms of satisfying diverse types of users.",
keywords = "Diversity, Multi armed bandit, Online learning, Recommendation system, Upper confidence bound",
author = "Mahmuda Rahman and Oh, {Jae C.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015 ; Conference date: 10-06-2015 Through 12-06-2015",
year = "2015",
doi = "10.1007/978-3-319-19066-2_35",
language = "English (US)",
isbn = "9783319190655",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "361--370",
editor = "Chang-Hwan Lee and Yongdai Kim and Kwon, {Young Sig} and Juntae Kim and Moonis Ali",
booktitle = "Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings",
}