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Maia, R. & Ferreira, J. C. (2018). Context-aware food recommendation system. In Lecture Notes in Engineering and Computer Science. (pp. 349-356). San Francisco: International Association of Engineers.

Rui and J. C. Ferreira,  "Context-aware food recommendation system", in Lecture Notes in Engineering and Computer Science, San Francisco, International Association of Engineers, 2018, pp. 349-356

@inproceedings{rui2018_1714156076966,
	author = "Maia, R. and Ferreira, J. C.",
	title = "Context-aware food recommendation system",
	booktitle = "Lecture Notes in Engineering and Computer Science",
	year = "2018",
	editor = "",
	volume = "",
	number = "",
	series = "",
	pages = "349-356",
	publisher = "International Association of Engineers",
	address = "San Francisco",
	organization = ""
}

TY  - CPAPER
TI  - Context-aware food recommendation system
T2  - Lecture Notes in Engineering and Computer Science
AU  - Maia, R.
AU  - Ferreira, J. C.
PY  - 2018
SP  - 349-356
SN  - 2078-0958
CY  - San Francisco
AB  - Recommendation systems are commonly used in websites with large datasets, frequently used in e-commerce or multimedia streaming services. These systems effectively help users in the task of finding items of their interest, while also being helpful from the perspective of the service or product provider. However, successful applications to other domains are less common, and the number of personalized food recommendation systems is surprisingly small although this particular domain could benefit significantly from recommendation knowledge. This work proposes a contextaware food recommendation system for well-being care applications, using mobile devices, beacons, medical records and a recommender engine. Users passing near a food place receives food recommendation based on available offers order by appropriate foods for everyone’s health at the table in real time. We also use a new robust recipe recommendation method based on matrix factorization and feature engineering, both supported by contextual information and statistical aggregation of information from users and items. The results got from the application of this method to three heterogeneous datasets of recipe’s user ratings, showed that gains are achieved regarding recommendation performance independently of the dataset size, the items textual properties or even the rating values distribution.
ER  -