A social recommendation system considering social relationship for online pet shopping

• Main Author: 李喬榆
• Other Authors: 柯皓仁
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/72836787487416211254

碩士 === 國立交通大學 === 資訊學院數位圖書資訊學程 === 104 === In recent years, the increasing popularity of social networking technologies and platforms has opened up a new era of electronic commerce, called social commerce, which changes the traditional thinking about online shopping. Social commerce uses the information such as user rating, social advertising and social friend relationship of social networks to assist in the buying of selling of products. The major difference between electronic commerce and social commerce is that consumers often rely on the advice and recommendations from online friends when making purchase decisions. Therefore, the traditional recommendation mechanism in electronic commerce is not suitable for the social commerce. This study proposes a social recommendation system that can generate personalized product recommendations based on the transactional purchase records from the legacy systems and social relationship from the social networking platforms, Facebook. Accordingly, our experiments employ the case study of an online pet shopping to show that the proposed social recommendation system outperforms traditional recommendation mechanism in terms of the precision and recall. The proposed social recommendation system can also be effectively applied to social-commerce retailers to promote their products and services in the social networks.