Abstract
Due to the ever-increasing amount of various information provided by the internet, recommendation systems are now used in a large number of fields as efficient tools to get rid of information overload. The content-based, collaborative-based and hybrid methods are the three classical recommendation techniques, whereas not all real-world problems (e.g. the food recommendation problem) can be best addressed by such classical recommendation techniques. This paper is devoted to solving the food recommendation problem based on many-objective optimization (MaOO). A novel recommendation approach is proposed by transforming the original recommendation problem into an MaOO one that contains four different objectives, i.e., the user preferences, nutritional values, dietary diversity, and user diet patterns. The experimental results demonstrate that the designed recommendation approach provides a more balanced way of recommending food than the classical recommendation methods that only consider individuals’ food preferences.
Original language | English |
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Pages (from-to) | 109-117 |
Number of pages | 9 |
Journal | Neurocomputing |
Volume | 503 |
Early online date | 29 Jun 2022 |
DOIs | |
Publication status | Published - 7 Sept 2022 |
Bibliographical note
Publisher Copyright:© 2022
Keywords
- Food recommendation
- Many-objective optimization
- Recommendation system
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence