Personalized time-aware outdoor activity recommendation system

Rahimiaghdam, Shakiba
The new growing generation of the communication technology has been gaining enormous popularity in the past few years. 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 surrenders 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, we present two approaches 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. We evaluated our systems on a medium-scale real data set gained by the combination of the Gowalla and Foursqaure. The experimental results confirm that both our proposed methods remarkably outperform the basic CF model. In addition, we study several extensions on the location-based techniques as the minor contributions of this thesis.


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Citation Formats
S. Rahimiaghdam, “Personalized time-aware outdoor activity recommendation system,” M.S. - Master of Science, Middle East Technical University, 2015.