| In electronic commerce (EC), negotiation can be performed to determine fair exchanges between trading partners. In order to negotiate autonomously on behalf of a user, an intelligent agent must obtain as much information as possible about the user's preferences over possible outcomes, but without asking the user an unreasonable number of questions. This thesis explores the idea of clustering partial preference relations as a means for predicting a user's preferences. Previously unknown preferences for a user can be predicted by observing those of similar users in the same cluster. Three techniques for clustering and predicting preferences are developed based on the Y-means clustering method, and a number of experiments are conducted. The MovieLens data set, normally used to test recommendation systems, is adapted for this domain and used to provide experiments with real subjects. Results show that one particular method, which predicts which of two outcomes is preferred by analyzing the confidence in average estimated utilities for users in the same cluster, is accurate 70-75% of the time when cluster data are sufficient for making a prediction (about 67% of the time). Another method, while maintaining a slightly lower prediction rate, is shown to be accurate 72-82% of the time, depending on the number of known preferences for clustered users. Statistical tests show that these results are significant. |
