On-line negotiation and bargaining systems can work effectively on the Internet based on the prerequisite that user utility functions are known while undergoing transactions. However, this prerequisite is hard to meet due to the variety and anonymous nature of Internet surfing. Therefore, how to rapidly and precisely construct a user¡¦s utility function is an essential issue. This research proposes a radial basis function (RBF) network, a neural network, to model a user¡¦s utility function in order to rapidly and precisely model user utility function. We verify the feasibility of the method through experiments, and compare the performance of RBF networks in prediction performance, time expenses, and subjects¡¦ perceptions with the Multiple Regression (MR), SMARTS, and SMARTER methods. The results show that the RBF network method is feasible in these criteria. Not only the RBF network needs less time to construct the users¡¦ utility function than the SMARTS method does, but also it can model user utility functions more precisely than the MR, SMARTS, and SMARTER methods.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0714104-152258 |
| Date | 14 July 2004 |
| Creators | Yang, Yu-chen |
| Contributors | Fu-ren Lin, Chin-fu Ho, Jen-her Wu |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714104-152258 |
| Rights | unrestricted, Copyright information available at source archive |
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