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Beyond Clothing Ontologies: Modeling Fashion with Subjective Influence Networks

Kurt Bollacker, Natalia Díaz Rodríguez, Xian Li, Beyond Clothing Ontologies: Modeling Fashion with Subjective Influence Networks. In: Vikas C. Raykar, Brad Klingenberg, Heng Xu, Raghavendra Singh, Amrita Saha (Eds.), Machine Learning meets fashion KDD Workshop, 1–7, ACM, 2016.

Abstract:

Extracting knowledge and actionable insights from fashion
data still presents challenges due to the intrinsic subjectivity needed to effectively model the domain. Fashion ontologies help address this, but most existing such ontologies are
\clothing" ontologies, which consider only the physical at-
tributes of garments or people and often model subjective
judgements only as opaque categorizations of entities. We
address this by proposing a supplementary ontological approach in the fashion domain based on subjective influence
networks. We enumerate a set of use cases this approach is
intended to address and discuss possible classes of prediction
questions and machine learning experiments that could be
executed to validate or refute the model.

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BibTeX entry:

@INPROCEEDINGS{inpBoDxLi16a,
  title = {Beyond Clothing Ontologies: Modeling Fashion with Subjective Influence Networks},
  booktitle = {Machine Learning meets fashion KDD Workshop},
  author = {Bollacker, Kurt and Díaz Rodríguez, Natalia and Li, Xian},
  editor = {Raykar, Vikas C. and Klingenberg, Brad and Xu, Heng and Singh, Raghavendra and Saha, Amrita},
  publisher = {ACM},
  pages = {1–7},
  year = {2016},
  keywords = {ontologies, recommender systems, knowledge graph, influence, subjectivity, fashion, clothing, networks},
  ISSN = {2154-817X},
}

Belongs to TUCS Research Unit(s): Embedded Systems Laboratory (ESLAB)

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