期刊名称:Lecture Notes in Engineering and Computer Science
印刷版ISSN:2078-0958
电子版ISSN:2078-0966
出版年度:2018
卷号:2231&2232
页码:349-356
出版社:Newswood and International Association of Engineers
摘要: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.