Abstract :
This paper presents a novel method of measuring user similarity in Location-Based Services (LBS) via relationships between users and annotation tags of locations they attended. Collecting all check-in data together, matrix factorization methods are applied in order to find semantic similarity between tags. Next, an idea of User Attendance Graph (UAG) is proposed to represent user check-in history and describe importance of each tag together with transitions between them. Further, Semantic Behavior Similarity (SBS) algorithm is proposed to measure likeness between UAG. This approach was evaluated with a real dataset collected from Whrrl using nDCG measure. Results show ~90% efficiency of proposed method for finding LBS users with similar behavior, and it can be used in different applications, e.g. Friend recommender systems.
