Improving location recommendations with temporal pattern extraction

  • Leandro Balby Marinho UFCG
  • Iury Nunes UFCG
  • Thomas Sandholm Palo Alto, USA
  • Caio Nóbrega UFCG
  • Jordão Araújo UFCG
  • Carlos Eduardo Pires UFCG


A key challenge in mobile social media applications is how to present personalized content that is both geographically and temporally relevant. In this paper, we propose a new and generic temporal weighting function for improving location recommendations. First, we identify areas of interest to recommend by clustering geographic activity based on a trace of geotagged photos. Next, the clusters are temporally weighted using TF-IDF, in order to capture seasonality, and a decay scoring function to capture preference drift. Finally, these weights are combined with the cluster scores based on geographic relevance. We evaluate our recommender on a large dataset collected from Panoramio consisting of the top-100 most populated cities in the world and show that incorporating the proposed temporal weighting function improves recommendation quality.
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MARINHO, Leandro Balby; NUNES, Iury; SANDHOLM, Thomas; NÓBREGA, Caio; ARAÚJO, Jordão; PIRES, Carlos Eduardo. Improving location recommendations with temporal pattern extraction. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 18. , 2012, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2012 . p. 293-296.