Trust No One: Evaluating Trust-based Filtering for Recommenders
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1663-1664.
Abstract
To be successful recommender systems must gain the trust of users. To do this they must demonstrate their ability to make reliable predictions. We argue that collaborative filtering recommendation algorithms can benefit from explicit models of trust to inform their predictions. We present one such model of trust along with a cost-benefit analysis that focuses on the classical trade-off that exists between recommendation coverage and prediction accuracy.
John O'Donovan, and Barry Smyth. 2005. Trust No One: Evaluating Trust-based Filtering for Recommenders. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05. IJCAI, 1663–1664. https://ijcai.org/proceedings/2005/321
O'Donovan, J., & Smyth, B. (2005). Trust No One: Evaluating Trust-based Filtering for Recommenders. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 (pp. 1663–1664). IJCAI. https://ijcai.org/proceedings/2005/321
@inproceedings{ijcai2005p321,
title = {Trust No One: Evaluating Trust-based Filtering for Recommenders},
author = {O'Donovan, John and Smyth, Barry},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1663--1664},
year = {2005},
url = {https://ijcai.org/proceedings/2005/321},
}
TY - CPAPER TI - Trust No One: Evaluating Trust-based Filtering for Recommenders AU - O'Donovan, John AU - Smyth, Barry T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1663 EP - 1664 UR - https://ijcai.org/proceedings/2005/321 ER -Copied
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