| dc.contributor | 資管系 | |
| dc.creator (作者) | 苑守慈 | zh_TW |
| dc.creator (作者) | Yuan, Soe-Tsyr;Liu, A | |
| dc.date (日期) | 2000-05 | |
| dc.date.accessioned | 5-Mar-2015 14:03:12 (UTC+8) | - |
| dc.date.available | 5-Mar-2015 14:03:12 (UTC+8) | - |
| dc.date.issued (上傳時間) | 5-Mar-2015 14:03:12 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=82308 | - |
| dc.description.abstract (摘要) | Agents are the catalysts for commerce on the Web today. For example, comparison-shopping agents mediate the interactions between buyers and sellers in order to yield more efficient markets. However, today`s shopping agents are price-dominated, unreflective of the nature of seller/buyer differentiation or the changing course of differentiation over time. This paper aims to tackle this dilemma and advances shopping agents into a stage where both kinds of differentiation are taken into account for enhanced understanding of the realities. We call them next-generation shopping agents. These agents can leverage the interactive power of the Web for more accurate understanding of buyer`s preferences. This paper then presents an architecture of the next-generation shopping agents. This architecture is composed of a Product/Merchant Information Collector, a Buyer Behavior Extractor, a User Profile Manager and an Online Learning Personalized-Ranking Module. We have implemented a system following the core of the architecture and collected preliminary evaluation results. The results show this system is quite promising in overcoming the reality challenges of comparison shopping. | |
| dc.format.extent | 264552 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Expert Systems with Applications,18(4),283-297 | |
| dc.subject (關鍵詞) | (Multi-) agent systems; Comparison shopping; Reinforcement learning; Neural networks; Buyer valuation models | |
| dc.title (題名) | Next-generation agent-enabled comparison shopping | |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1016/S0957-4174(00)00010-5 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1016/S0957-4174(00)00010-5 | en_US |
