Swarm intelligence clustering ensemble based point of interest recommendation for social cyber-physical systems

Cyber-physical Social Network (CPSN) becomes an essential component of daily life. In recent years, CPSN has dragged millions of users to convey their social opinions. It is highly desirable to mine influential features from such diverse data to make a prediction on users’ Point of Interest (POI). Notably, social ties of the user in a specific location have a tendency to share similar opinions. Thus with the appearance of social links, the location based recommendations become popular to acquire reliable POI recommendations. Collaborative Filtering Recommender System (CFRS) able to discover reliable POI recommendations for the target user based on Location-based CPSN. To enhance the performance of CFRS, a clustering ensemble model is proposed in this article. Four different swarm intelligent based cluster optimization algorithms were utilized to generate finite clusters. The experiment is conducted on two real-time social network dataset to exhibit the performance of the proposed CE-CFRS. The result shows that the clustering ensemble model outperforms a single clustering model in terms of assessment metrics.