Authors:
Lucas Silva Couto
;
Gislaine Leal
and
Marcos Aurélio Domingues
Affiliation:
State University of Maringá, Department of Informatics, Maringá, Brazil
Keyword(s):
Recurrent Neural Networks, Context-Aware Recommender Systems, Points of Interest, Context Acquisition, Embeddings.
Abstract:
The advent of location-based social networks (LBSNs) has reshaped how users engage with their surroundings, facilitating personalized connections with nearby points of interest (POIs) like restaurants, tourist attractions, and so on. To help the users to find points that fit their interests, recommender systems can be used to filter a large number of POIs according to the users’ preferences. However, the context in which the users make their check-ins must be taken into account, which justifies the usage of context-aware recommender systems. The goal of this work is to use a Context-Aware Dual Recurrent Neural Network to acquire contextual information (represented by embeddings) for each POI, given the sequence of points that each user has checked-in. Then, the contextual information (i.e. the embeddings) is used by context-aware recommenders to suggest POIs. We evaluated the contextual information by using four context-aware recommender systems in two datasets. The results showed th
at the contextual information obtained by our proposed method presents better results than the state-of-the-art method proposed in the literature.
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