The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efficiency. We aim to design a novel model representation of the personal context and design a learning process for better integration with machine learning. We aim to implement these elements into a modern system architecture focus in real-life environments. Also, we show how our proposal can improve in specifically related work papers. Finally, we are moving forward with a better personal context representation with an improved model, the implementation of the learning process, and the architectural design of these components.
@article{arxiv.2108.08234,
title = {Streaming and Learning the Personal Context},
author = {Fausto Giunchiglia and Marcelo Rodas Britez and Andrea Bontempelli and Xiaoyue Li},
journal= {arXiv preprint arXiv:2108.08234},
year = {2021}
}