Automating the Discovery of Recommendation Knowledge

McSherry, David; Stretch, Christopher

Automating the Discovery of Recommendation Knowledge

Download BibTeX Cite

Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 9-13.

Abstract

In case-based reasoning (CBR) systems for product recommendation, the retrieval of acceptable products based on limited information is an important and challenging problem. As we show in this paper, basic retrieval strategies such as nearest neighbor are potentially unreliable when applied to incomplete queries. To address this issue, we present techniques for automating the discovery of recommendation rules that are provably reliable and non-conflicting while requiring minimal information for their application in a rule-based approach to the retrieval of recommended cases.

David McSherry, and Christopher Stretch. 2005. Automating the Discovery of Recommendation Knowledge. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05. IJCAI, 9–13. https://ijcai.org/proceedings/2005/88

@inproceedings{ijcai2005p88,
  title     = {Automating the Discovery of Recommendation Knowledge},
  author    = {McSherry, David and Stretch, Christopher},
  booktitle = {Proceedings of the Nineteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-05}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  pages     = {9--13},
  year      = {2005},
  url       = {https://ijcai.org/proceedings/2005/88},
}

TY  - CPAPER
TI  - Automating the Discovery of Recommendation Knowledge
AU  - McSherry, David
AU  - Stretch, Christopher
T2  - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2005
SP  - 9
EP  - 13
UR  - https://ijcai.org/proceedings/2005/88
ER  -