Development of social-aware recommendation system using public preference mining and social influence analysis: A case study of landscape recommendation

A novel social-aware recommendation system framework which employs public preference and social influence to classify user preference orientation is proposed in this paper. We collected large scale information from heterogeneous data sources to construct the public preference model for user's feature-based preference orientation classification. Moreover, the social relation graph of target user is constructed to analyze social influence of preference between users in it. Then, the social influence of preference is calculated by social influence and interest similarity between users. In addition, the preference guiding pair is constructed by real data to be the baseline of preference adjustment. The purpose of this paper is that using public preference to infer user preference and further adjusting user preference through social influence of preference from neighbors. In our experiment, blogs, news and online social networks are the information sources to construct public preference model. Moreover, Facebook, the most famous social media, is the platform selected for social relationship analysis. The experimental result shows our approach not only innovation but also practicable.