A Framework for Recommending Relevant and Diverse Items
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3868-3873.
Abstract
The traditional recommendation systems usually aim to improve the recommendation accuracy while overlooking the diversity within the recommended lists. Although some diversification techniques have been designed to recommend top-k items in terms of both relevance and diversity, the coverage of the user's interest is overlooked. In this paper, we propose a general framework to recommend relevant and diverse items which explicitly takes the coverage of user interest into account. Based on the theoretical analysis, we design efficient greedy algorithms to get the near optimal solutions for those NP-hard problems. Experimental results on MovieLens dataset demonstrate that our approach outperforms state-of-the-art techniques in terms of both precision and diversity.
@inproceedings{ijcai2016p544,
title = {A Framework for Recommending Relevant and Diverse Items},
author = {Sha, Chaofeng and Wu, Xiaowei and Niu, Junyu},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3868--3873},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/544},
}
TY - CPAPER TI - A Framework for Recommending Relevant and Diverse Items AU - Sha, Chaofeng AU - Wu, Xiaowei AU - Niu, Junyu T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3868 EP - 3873 UR - https://ijcai.org/proceedings/2016/544 ER -Copied
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