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Title: Reproducibility companion paper : recommendation of mix-and-match clothing by modeling indirect personal compatibility
Authors: Liao, S 
Ding, Y 
Mok, PY 
Huang, Q
Cao, J
Issue Date: 2024
Source: In ICMR '24: Proceedings of the 14th Annual: ACM International Conference on Multimedia Retrieval, p. 1224-1227. New York, NY: The Association for Computing Machinery, 2024
Abstract: This 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.
Keywords: Compatibility
Complementary Recommendation
Fashion Recommendation
Multi-modal
Personalization
Publisher: Association for Computing Machinery
ISBN: 979-8-4007-0619-6
DOI: 10.1145/3652583.3658371
Description: ICMR '24: International Conference on Multimedia Retrieval, Phuket, Thailand, June 10-14, 2024
Rights: © 2024 Copyright held by the owner/author(s).
This work is licensed under a Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/).
The 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.
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