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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/73672


    Title: Next-generation agent-enabled comparison shopping
    Authors: 苑守慈
    Yuan, Soe-Tsyr;Liu, A
    Contributors: 資管系
    Keywords: (Multi-) agent systems;Comparison shopping;Reinforcement learning;Neural networks;Buyer valuation models
    Date: 2000-05
    Issue Date: 2015-03-05 14:03:12 (UTC+8)
    Abstract: 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.
    Relation: Expert Systems with Applications,18(4),283-297
    Data Type: article
    DOI link: http://dx.doi.org/10.1016/S0957-4174(00)00010-5
    DOI: 10.1016/S0957-4174(00)00010-5
    Appears in Collections:[Department of MIS] Periodical Articles

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