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Learning User Preferences for Sets of Objects (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Eaton, Eric Wagstaff, Kiri L. |
| Description | Proceedings of the Twenty-Third International Conference on Machine Learning Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of items. Our learning method takes as input a collection of positive examples—that is, one or more sets that have been identified by a user as desirable. Kernel density estimation is used to estimate the value function for individual items, and the desired set diversity is estimated from the average set diversity observed in the collection. Since this is a new learning problem, we introduce a new evaluation methodology and evaluate the learning method on two data collections: synthetic blocks-world data and a new real-world music data collection that we have gathered. 1. |
| File Format | |
| Language | English |
| Publisher | ACM |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | New Real-world Music Data Collection Positive Example New Learning Problem Data Collection Synthetic Blocks-world Data Desired Set Diversity Average Set Diversity Pairwise Preference Kernel Density Estimation Value Function Preference Learning New Evaluation Methodology User Preference Individual Item Learning Method |
| Content Type | Text |
| Resource Type | Article |
