Title :
Style matching model-based recommend system for online shopping
Author :
Zhao, Guannan ; Luo, Shijian ; He, Ji
Author_Institution :
Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
Abstract :
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.
Keywords :
content-based retrieval; electronic commerce; feature extraction; image colour analysis; image retrieval; image texture; recommender systems; retail data processing; commodity cluster; content-based image retrieval; e-commerce functionality; image feature extraction; online shopping; product color; product shape; product texture; shoe buying recommend system; style matching model-based recommend system; Content based retrieval; Data mining; Feature extraction; Feedback; Helium; Image databases; Image retrieval; Information retrieval; Search engines; User interfaces; E-commerce; Image Retrieval; Recommend system; Style matching model;
Conference_Titel :
Computer-Aided Industrial Design & Conceptual Design, 2009. CAID & CD 2009. IEEE 10th International Conference on
Conference_Location :
Wenzhou
Print_ISBN :
978-1-4244-5266-8
Electronic_ISBN :
978-1-4244-5268-2
DOI :
10.1109/CAIDCD.2009.5375128
