Abstract
This paper presents an unsupervised approach toaccurately discover interesting places in a city from location-basedsocial sensing applications, a new sensing applicationparadigm that collects observations of physical world fromLocation-based Social Networks (LBSN). While there are alarge amount of prior works on personalized Point of Interests(POI) recommendation systems, they used supervised learningapproaches that did not work for users who have little orno historic (training) data. In this paper, we focused onan interesting place discovery problem where the goal isto accurately discover the interesting places in a city thataverage people may have strong interests to visit (e.g., parks, museums, historic sites, etc.) using unsupervised approaches. Inparticular, we develop a new Physical-Social-aware InterestingPlace Discovery (PSIPD) scheme which jointly exploits thelocation's physical dependency and the visitor's social dependencyto solve the interesting place discovery problem using anunsupervised approach. We compare our solution with state-of-the-art baselines using two real world data traces from LBSN. The results showed that our approach achieved significantperformance improvements compared to all baselines in termsof both estimation accuracy and ranking performance.