An Efficient Service Recommendation Algorithm for Cyber-Physical-Social Systems

Cyber-physical-social System (CPSS) technology closely integrates and coordinates computing resources, physical resources, and social information to provide humans with efficient and convenient services. Service recommendation technology satisfies the individual needs of users by collecting, processing, and analyzing user characteristic data. In recent years, service recommendation algorithms based on social networks and collaborative filtering have been widely used in CPSS. However, this type of service recommendation algorithm cannot effectively use the aliasing and auxiliary information in the service, nor can it generate different service recommendation schemes according to different scenarios. We propose a collaborative filtering service recommendation algorithm that combines heterogeneous information networks and topic models for CPSS. The proposed algorithm designs a more effective functional similarity measurement method through the word vectors learned by Word2vec. Then, we use the topic model to cluster the scenario-based service (SBS), thereby greatly reducing the time required for service recommendation. Experimental analysis results show that we finally determine the best combination of Mashup similarity between the two scenarios with better recommendation efficiency and accuracy. Thus, the proposed recommendation algorithm can effectively improve the quality of service of CPSS, and provide a basis for the personalized service of CPSS.