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DC Field | Value | Language |
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dc.contributor.author | Zhang, J | - |
dc.contributor.author | Li, M | - |
dc.contributor.author | Liu, W | - |
dc.contributor.author | Lauria, S | - |
dc.contributor.author | Liu, X | - |
dc.date.accessioned | 2022-08-12T13:21:06Z | - |
dc.date.available | 2022-08-12T13:21:06Z | - |
dc.date.issued | 2022-06-29 | - |
dc.identifier.citation | Zhang, J., Li, M., Liu, W., Lauria, S. and Liu, X. (2022) 'Many-objective optimization meets recommendation systems: A food recommendation scenario', Neurocomputing, 503, pp. 109 - 117. doi: 10.1016/j.neucom.2022.06.081. | en_US |
dc.identifier.issn | 0925-2312 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25072 | - |
dc.description.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. | en_US |
dc.format.extent | 109 - 117 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier B.V. | en_US |
dc.rights | Crown Copyright © 2022 Published by Elsevier B.V. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | food recommendation | en_US |
dc.subject | recommendation system | en_US |
dc.subject | many-objective optimization | en_US |
dc.title | Many-objective optimization meets recommendation systems: A food recommendation scenario | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.neucom.2022.06.081 | - |
dc.relation.isPartOf | Neurocomputing | - |
pubs.publication-status | Published | - |
pubs.volume | 503 | - |
dc.identifier.eissn | 1872-8286 | - |
dc.rights.holder | Crown | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Crown Copyright © 2022 Published by Elsevier B.V. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | 2.33 MB | Adobe PDF | View/Open |
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