@inproceedings{76b06b2d95bb477884eefd96ea77ad9b,
title = "Personalized knowledge discovery: Mining novel association rules from text",
abstract = "This paper presents a methodology for personalized knowledge discovery from text. It derives a user's background knowledge from his/her background documents, and exploits such knowledge to evaluate the novelty of discovered knowledge in the form of association rules by measuring the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment results show that the proposed user-oriented novelty measure is highly correlated with the human subjective rule novelty and usefulness ratings. It outperforms seven major objective interestingness measures and the WordNet novelty measure for identifying novel and useful rules.",
keywords = "Association Rule Mining, Interestingness, Novelty, Personalization, Text Mining",
author = "Xin Chen and Wu, {Yi Fang}",
year = "2006",
doi = "10.1137/1.9781611972764.66",
language = "English (US)",
isbn = "089871611X",
series = "Proceedings of the Sixth SIAM International Conference on Data Mining",
publisher = "Society for Industrial and Applied Mathematics",
pages = "589--593",
booktitle = "Proceedings of the Sixth SIAM International Conference on Data Mining",
note = "Sixth SIAM International Conference on Data Mining ; Conference date: 20-04-2006 Through 22-04-2006",
}