基于在线客户情绪能量感知的商品推荐算法  

E-Commerce Recommendation Algorithm Based on On-line Client Emotion Energy Sensory Extensions

作　　者：王征[1,2] 谷安平[3] 刘心松[2] 

机构地区：[1]西南财经大学经济信息工程学院 [2]电子科技大学8010研究室,成都610054 [3]西南财经大学统计学院,成都610074

出　　处：《吉林大学学报（信息科学版）》2009年第3期262-267,共6页Journal of Jilin University（Information Science Edition）

基　　金：四川省应用基础研究基金资助项目(04JY029-017-2);科技型中小企业技术创新基金资助项目(04C26225110223)

摘　　要：为了解决基于传统数据挖掘方法的电子商务推荐算法时效性差、准确度不高的问题,提出了基于情绪能量感知的推荐算法。该算法能实时判断在线用户的购买倾向;通过情感能量匹配技术,对商品特征进行分类,并与用户情绪状态进行匹配。理论性能分析和实验证明,该算法较之传统方法具有较高的匹配准确度、较好的时效性和用户满意度。Based on data mining methods, traditional e-commerce recommendation algorithms work unreal-timely and imprecisely. In order to solve the problems, an improved algorithm ESER was presented, which utilized emotion energy sensory extensions to measure online-client emotion and to manage shopping tendencies. Then it classified ware characteristics and matched them with shopping information in a uniform emotion energy and ware characteristic space. Performance analysis and simulation results show that it can provide better real-time and customer satisfaction than the traditional does. And it can match customers and wares better than the traditional does.

关 键 词：分布式计算 情绪计算 电子商务 推荐算法 匹配 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]