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Reputation model for C2C commerce with filtering merchandise categories

机译:过滤商品类别的C2C商业的声誉模型

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With the large number of buyer and seller agents in C2C commerce, the process managing to establishing trust relations using previous repeated direct interactions is difficult to achieve. In a loosely connected e-market, the best way to establish trust relations is probably with the help of the commendation coming from the buyer agents who have traded with the same seller agent. Furthermore, the effectiveness of the commendation is partly determined by the similarity of contents between historical and present trades — context relevance which is identified through the merchandise categories in this paper. With the concepts — buyer's merchandise-categories reputation and buyer's recommended reputation filtering merchandise categories — being proposed, and the experience cumulative function used for calculating the buyer's recommended reputation and the equation used for aggregating seller's reputation being designed, a reputation system based on SPORAS is improved. Finally, compared with SPORAS, the benefits of filtering merchandise categories to manage reputation in C2C commerce are analyzed according to the excellent experimental results shown.
机译:随着C2C商业中的大量买家和卖方代理商,难以实现使用以前重复的直接互动建立信任关系的过程是难以实现的。在一个松散连接的电子市场中,建立信任关系的最佳方式可能是借助来自与同一卖方代理商交易的买家代理商的赞誉。此外,求和的有效性是由历史和目前交易之间的内容的相似性的部分决定 - 上下文相关性通过本文通过商品类别确定。凭借这些概念 - 买方的商品类别声誉和买方推荐的声誉过滤商品类别 - 正在提出,以及用于计算买方推荐声誉的经验累积功能和用于汇总卖方声誉的方程,这是一种基于Sporas的声誉系统改善。最后,与Sporas相比,根据所示的优秀实验结果,分析了过滤商品类别来管理C2C商业中声誉的好处。

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