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In recent years, an extensive integration of cyber, physical and social spaces has been occurring. Cyber-Physical-Social Systems (CPSSs) have become the basic paradigm of evolution in the information industry, through which traditional computer science will evolve into cyber-physical-social computational science. Intelligent recommender systems, which are an important fundamental research topic in the CPSS field and one of the key techniques for the implementation of personalized and intelligent computing, have great significance in CPSS development. This paper proposes a group-centric recommender system in the CPSS domain, which consists of activity-oriented group discovery, the revision of rating data for improved accuracy, and group preference modeling that supports sufficient context mining from multiple sources. Through experiments, it is verified that the proposed recommender system is efficient, objective and accurate, thereby providing a strong foundation for personalized computing in the CPSS paradigm.
Yin Zhang⋆ (Mon,) studied this question.