Deep Ranking for Style-Aware Room Recommendations (Student Abstract)

Authors

  • İlkay Yıldız Northeastern University
  • Esra Ataer-Cansızoğlu Wayfair LLC
  • Hantian Liu Wayfair LLC
  • Peter Golbus Wayfair LLC
  • Ozan Tezcan Boston University
  • Jae-Woo Choi Wayfair LLC

DOI:

https://doi.org/10.1609/aaai.v34i10.7260

Abstract

We present a deep learning based room image retrieval framework that is based on style understanding. Given a dataset of room images labeled by interior design experts, we map the noisy style labels to comparison labels. Our framework learns the style spectrum of each image from the generated comparisons and makes significantly more accurate recommendations compared to discrete classification baselines.

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Published

2020-04-03

How to Cite

Yıldız, İlkay, Ataer-Cansızoğlu, E., Liu, H., Golbus, P., Tezcan, O., & Choi, J.-W. (2020). Deep Ranking for Style-Aware Room Recommendations (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13975-13976. https://doi.org/10.1609/aaai.v34i10.7260

Issue

Section

Student Abstract Track