Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
PHASES: A User Profile Learning Approach for Web Search
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
On Minimizing the Position Error in Label Ranking
ECML '07 Proceedings of the 18th European conference on Machine Learning
Evaluating Natural User Preferences for Selective Retrieval
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Learning User Preferences for 2CP-Regression for a Recommender System
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Evaluating top-k algorithms with various sources of data and user preferences
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Expert Systems with Applications: An International Journal
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In this paper we deal with the problem of learning user preferences from user’s scoring of a small sample of objects with labels from a very small linearly ordered set. The main task of this process is to use these preferences for a top-k query, which delivers the user with an ordered list of k highest ranked objects. We deal with a problem of many ties in the highest score. Two algorithms for learning objective and utility functions are presented. We experiment and compare them to some classical data-mining methods. We use several measures (RMSE and rank correlations …) to evaluate efficiency of these methods.