Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/109214
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dc.contributorSchool of Fashion and Textilesen_US
dc.creatorLiao, Sen_US
dc.creatorDing, Yen_US
dc.creatorMok, PYen_US
dc.creatorHuang, Qen_US
dc.creatorCao, Jen_US
dc.date.accessioned2024-09-24T04:20:57Z-
dc.date.available2024-09-24T04:20:57Z-
dc.identifier.isbn979-8-4007-0619-6en_US
dc.identifier.urihttp://hdl.handle.net/10397/109214-
dc.descriptionICMR '24: International Conference on Multimedia Retrieval, Phuket, Thailand, June 10-14, 2024en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.rights© 2024 Copyright held by the owner/author(s).en_US
dc.rightsThis work is licensed under a Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Liao, S., Ding, Y., Mok, P. Y., Huang, Q., & Cao, J. (2024). Reproducibility Companion Paper: Recommendation of Mix-and-Match Clothing by Modeling Indirect Personal Compatibility Proceedings of the 2024 International Conference on Multimedia Retrieval, Phuket, Thailand is available at https://doi.org/10.1145/3652583.3658371.en_US
dc.subjectCompatibilityen_US
dc.subjectComplementary Recommendationen_US
dc.subjectFashion Recommendationen_US
dc.subjectMulti-modalen_US
dc.subjectPersonalizationen_US
dc.titleReproducibility companion paper : recommendation of mix-and-match clothing by modeling indirect personal compatibilityen_US
dc.typeConference Paperen_US
dc.identifier.spage1224en_US
dc.identifier.epage1227en_US
dc.identifier.doi10.1145/3652583.3658371en_US
dcterms.abstractThis reproducibility companion paper accompanies our original study, "Recommendation of Mix-and-Match Clothing by Modeling Indirect Personal Compatibility," providing a detailed framework for replication and verification of our research results. The primary objective of this document is to enhance the transparency and reproducibility of our findings. We present a comprehensive account of the datasets, software tools, and experiments in the original study. This companion paper serves as a valuable resource for researchers and practitioners who aim to validate, learn from, or build upon our work.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn ICMR '24: Proceedings of the 14th Annual: ACM International Conference on Multimedia Retrieval, p. 1224-1227. New York, NY: The Association for Computing Machinery, 2024en_US
dcterms.issued2024-
dc.identifier.scopus2-s2.0-85199137865-
dc.relation.ispartofbookICMR '24: Proceedings of the 14th Annual: ACM International Conference on Multimedia Retrievalen_US
dc.relation.conferenceInternational Conference on Multimedia Retrieval [ICMR]en_US
dc.description.validate202409 bcchen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_TA-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextInnovation and Technology Commission of Hong Kong; InnoHK Research Clusters, Hong Kong Special Administrative Region Government; National Natural Science Foundation of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.description.TAACM (2024)en_US
dc.description.oaCategoryTAen_US
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