Context-Category Specific sequence aware Point-Of-Interest Recommender System with Multi-Gated Recurrent Unit

Abstract

Point-Of-Interest (POI) Recommender Systems (RSs) have huge importance in Location Based Social Networks (LBSNs) because of its effectiveness in assisting users to explore personalized locations. It also assists the LBSN providers to increase their revenue through scrutinized advertisements or services according to specific locations. For the effectiveness and accuracy of POI RSs, so many additional information such as Transition Contexts (e.g., geographical distance, time interval), Dynamic Contexts (e.g., time of the day, companion, season), and Static Contexts (e.g., POI type, features) have to be integrated along with the check-in data. The high impact of this additional information distinguishes POI recommendation approaches from other RS approaches. To address the challenges of the varying influences of the user’s current contexts and the transition contexts (arises from users past task to current task) on recommendation, a Gated Recurrent Unit (GRU) architecture is proposed. It is capable of handling the effect of each category of contexts separately. The main part of the proposed Context-Category Specific Sequence Aware POI RS (CCS-POI-RS) is a Multi-GRU (MGRU), which has two added gates for handling the influences of both dynamic contexts and transition contexts. Experiments on Gowalla and Foursquare check-in data set reveal the significance of MGRU architecture through the comparison with the other state of the art GRU architectures.