A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network

Abstract

In this paper, we build a recommender system for a new study area: social commerce, which combines rich information about social network users and products on an e-commerce platform. The idea behind this recommender system is that a social network contains abundant information about its users which could be exploited to create profiles of the users. For social commerce, the quality of the profiles of potential consumers determines whether the recommender system is a success or a failure. In our work, not only the user’s textual information but also the tags and the relationships between users have been considered in the process of building user profiling model. A topic model has been adopted in our system, and a feedback mechanism also been design in this paper. Then, we apply a collative filtering method and a clustering algorithm in order to obtain a high recommendation accuracy. We do an empirical analysis based on real data collected on a social network and an e-commerce platform. We find that the social network has an impact on e-commerce, so social commerce could be realized. Simulations show that our topic model has a better performance in topic finding, meaning that our profile-building model is suitable for a social commerce recommender system.

Keywords

• social commerce
• recommender system
• topic model
• CFUA