Towards a knowledge graph-based approach for context-aware points-of-interest recommendations
Lavdim Halilaj, Jürgen Lüttin, Susanne Rothermel, Santhosh Kumar Arumugam, Ishan Dindorkar · 2021
Context-aware Recommender Systems (CARS) are becoming an integral part of the everyday life by providing users the ability to retrieve relevant information based on their contextual situation. To increase the predictive power considering many parameters, such as mood, hunger level and user preferences, information from heterogeneous sources should be leveraged. However, these data sources are typically isolated and unexplored and the efforts for integrating them are exacerbated by variety of data structures used for their modelling and costly pre-processing operations. We propose a Knowledge Graph-based approach to allow integration of data according to abstract semantic models for Points-of-Interests (POI)s recommendation scenarios. By enriching data with information about attributes, relationships and their meaning, additional knowledge can be derived from what already exists. We demonstrate the applicability of the proposed approach with a concrete example showing benefits of the retrieving the dispersed data with a unified access mechanism.