Influence-Time-Proximity Driven Locations Recommendation Model: An Integrated Approach
TL;DR: This paper proposes an integrated location recommendation model that considers users' interests, their friends influences, time and seasonality factors, and users' willingness to visit distant locations to generate a ranked list of locations which will be recommended to the user.
Abstract: Location Based Social Networks (LBSNs) like Twitter, Foursquare or Instagram are a very good source of extracting human generated data in the form of check-ins, location and social relationships among users. Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users preference and behaviour. There are many underlying patterns in this type of dataset of human mobility which are utilised for applications like recommender systems. The current approaches involve extracting data from user-item rating, GPS trajectories, or other forms of data, whereas we focus on an integrated model considering factors like user's interest, social influence, time and proximity. There is metadata associated with this dataset which tells us about the whereabouts of the user, with emphasis on types of places. This paper proposes an integrated location recommendation model that considers users' interests, their friends influences, time and seasonality factors, and users' willingness to visit distant locations. We integrate all these parameters to generate a ranked list of locations which will be recommended to the user. Experiments are performed on a real-world dataset which show that our proposed model is effective in the stated conditions.
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References
2 citations
"Influence-Time-Proximity Driven Loc..." refers background in this paper
...These patterns can further be used in designing of future mobile location based services, traffic forecasting or urban planning [8]....
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2 citations
"Influence-Time-Proximity Driven Loc..." refers background in this paper
...The underlying patterns of LBSNs allow us to analyse the effect of friends influence, centrality and spatial check-ins overlap on the users and correlate social network properties and external changes with users influences and check-in patterns [5]....
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