计算机科学
匹配(统计)
风格(视觉艺术)
情报检索
模式(计算机接口)
产品(数学)
图像检索
电子商务
推荐系统
特征提取
特征(语言学)
图像(数学)
多媒体
人工智能
人机交互
万维网
数学
考古
历史
哲学
统计
语言学
几何学
作者
Guannan Zhao,Shijian Luo,Ji He
标识
DOI:10.1109/caidcd.2009.5375128
摘要
In order to enrich the recommendation of e-commerce functionality and optimize the user experience of online shopping, a new style matching model-based recommend method of e-commerce was proposed. The Content-Based Image Retrieval (CBIR) technology was used to extract the image feature of color, texture and shape of product, calculated the commodity cluster, which have the similar feature of image content, then was applied to the style matching model, recommend to the user in accordance with the user's cognitive style. The style matching mode-based recommendation engine was developed, and integrated into the e-commerce recommend system to help users complete the cognitive of style and guide the online shopping. Finally, a shoe buying recommend system was developed to verify the theory.
科研通智能强力驱动
Strongly Powered by AbleSci AI