@inproceedings{591dbfd06dba43be9a486ddbaf1e351e,
title = "Group recommendations via multi-armed bandits",
abstract = "We study recommendations for persistent groups that repeatedly engage in a joint activity. We approach this as a multi-arm bandit problem. We design a recommendation policy and show it has logarithmic regret. Our analysis also shows that regret depends linearly on d, the size of the underlying persistent group. We evaluate our policy on movie recommendations over the MovieLens and MoviePilot datasets. Copyright is held by the author/owner(s).",
keywords = "Group recommendation, Multi-armed bandits",
author = "Jos{\'e} Bento and Stratis Ioannidis and S. Muthukrishnan and Jinyun Yan",
year = "2012",
doi = "10.1145/2187980.2188078",
language = "English (US)",
isbn = "9781450312301",
series = "WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion",
pages = "463--464",
booktitle = "WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion",
note = "21st Annual Conference on World Wide Web, WWW'12 ; Conference date: 16-04-2012 Through 20-04-2012",
}