Personalized time-aware outdoor activity recommendation system

Abstract

Location-based social network as one of the platforms in this field has been providing services and facilities to enhance user experience to explore their surroundings and new places. Among current services, point of interest (POI) recommendation and activity recommendation draws significant attention of users, which makes it a potential field of the study. However, despite of all the developments performed in this field, activity recommendation system still requires further improvements, since only a few related studies concentrated on this topic so far. In order to develop the activity recommendation system, in this work two approaches are presented, using different ideas by extending existing location-based collaborative filtering (CF) recommendation models. One of them focuses on the temporal feature of the data and the other one emphasizes on the correlation between activities to estimate the probability of selecting each activity. The proposed methods are evaluated on a medium-scale real data set obtained by combining the Gowalla and Foursqaure. The experimental results confirm that both methods remarkably outperform the basic CF model.