Exploiting Sequential Mobility for Recommending new Locations on Geo-tagged Social Media
Carmela Comito · 2020
The aim of the paper is to provide a novel location recommendation system exploiting user preference, social relationships and geographic constraints in social media. User preference and social ties are learned from the visited location history also accounting venues proximity to previous check-ins. A framework integrating sequential mobility and user preference is proposed. The framework formulates the recommendation task as a similarity problem among the visiting and mobility profiles of users, accounting the mobility sequentiality in the patterns. Two ranking metrics are introduced to predict places the user could like. The metrics are then combined into an overall recommendation ranking function. The candidate locations are then ranked according to the two similarity measures. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending unseen locations, outperforming representative state-of-the-art approaches.