World Wide Web

, Volume 21, Issue 2, pp 345–371 | Cite as

Recommendations based on a heterogeneous spatio-temporal social network

  • Pavlos KefalasEmail author
  • Panagiotis Symeonidis
  • Yannis Manolopoulos


Recommender systems in location-based social networks (LBSNs), such as Facebook Places and Foursquare, have focused on recommending friends or locations to registered users by combining information derived from explicit (i.e. friendship network) and implicit (i.e. user-item rating network, user-location network, etc.) sub-networks. However, previous models were static and failed to adequately capture user time-varying preferences. In this paper, we provide a novel recommendation method based on the time dimension as well. We construct a hybrid tripartite (i.e., user, location, session) graph, which incorporates 7 different unipartite and bipartite graphs. Then, we test it with an extended version of the Random Walk with Restart (RWR) algorithm, which randomly walks through the network by using paths of 7 differently weighted edge types (i.e., user-location, user-session, user-user, etc.). We evaluate experimentally our method and compare it against three state-of-the-art algorithms on two real-life datasets; we show a significant prevalence of our method over its competitors.


Algorithms Link prediction Location recommendation Friend recommendation Social networks Big data 



This research has benefited from discussions in the working groups of ICT COST Action IC1406 on High-Performance Modeling and Simulation for Big Data Applications (cHiPSet).


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of InformaticsAristotle UniversityThessalonikiGreece

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