Abstract
The importance of recommender system on Social Network Services (SNS) platform has been recognized by researchers and practitioners in many disciplines, including ecommerce, information retrieval, social computing, data mining, marketing, etc. While a substantial amount of approaches focus on recommending the most relevant items to users on mainstream SNS platforms, there is still a lack of closer investigation into the context-aware strategies on professional SNS platform whose contextual information varies significantly from generic SNS platforms. Drawing upon existing algorithmic paradigms –content-based methods and collaborative filtering, this paper proposes context-aware strategies that cope with the need to recommend both users and items on a professional SNS platform. A case study has been demonstrated based on such approach and directions for future research have been discussed.