Exploiting the User Activity-Level to Improve the Models’ Accuracy in Point-Of-Interest Recommender Systems - Chaves, Luiz; Silva, Nícollas; Carvalho, Rodrigo; Pereira, Adriano C. M.; Rocha, Leonardo (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2019: Proceedings of the 25th Brazilian Symposium on Multimedia and the Web, 2019)
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.