Exploiting the User Activity-Level to Improve the Models’ Accuracy in Point-Of-Interest Recommender Systems

  • Luiz Chaves UFSJ
  • Nícollas Silva UFMG
  • Rodrigo Carvalho UFSJ
  • Adriano C. M. Pereira UFMG
  • Leonardo Rocha UFSJ

Resumo


Recommender Systems (RS) have been applied in several scenarios due to their ability to satisfy the user’s interest. Traditionally, they have been applied in scenarios such as entertainment and ecommerce, and nowadays, in Location Based Social Network (LBSN) to recommend points-of-interest (POIs). Despite the advances in traditional scenarios, there is an opportunity for improvements in POI domains. For this scenario, it is necessary to consider the geography influence of POIs. However, we observe the main proposals are not able to achieve satisfactory results. In this work, we open a new research perspective in POI Recommendation area, proposing a post-processing approach that can be used with any RS. Basically, we measure the activity level of users in different subareas of a city and use it to re-order the POIs retrieved by a RS. We evaluate our proposal considering six recommender systems and three datasets from Yelp achieving gains up to 15% of precision.
Publicado
29/10/2019
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CHAVES, Luiz; SILVA, Nícollas; CARVALHO, Rodrigo; PEREIRA, Adriano C. M.; ROCHA, Leonardo. Exploiting the User Activity-Level to Improve the Models’ Accuracy in Point-Of-Interest Recommender Systems. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA) , 2019, Rio de Janeiro. Anais do XXV Simpósio Brasileiro de Multimídia e Web. Porto Alegre: Sociedade Brasileira de Computação, oct. 2019 . p. 341-348.

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