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

Ilkay Yildiz, Esra Ataer Cansizoglu, Hantian Liu, Peter B. Golbus, Ozan Tezcan, Jae-Woo Choi

AAAI 2020 pp. 13975-13976

doi:10.1609/AAAI.V34I10.7260 /aaai/2020/yildiz2020aaai-deep/

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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Cite

Text

Yildiz et al. "Deep Ranking for Style-Aware Room Recommendations (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7260

Markdown

[Yildiz et al. "Deep Ranking for Style-Aware Room Recommendations (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/yildiz2020aaai-deep/) doi:10.1609/AAAI.V34I10.7260

BibTeX

@inproceedings{yildiz2020aaai-deep,
  title     = {{Deep Ranking for Style-Aware Room Recommendations (Student Abstract)}},
  author    = {Yildiz, Ilkay and Cansizoglu, Esra Ataer and Liu, Hantian and Golbus, Peter B. and Tezcan, Ozan and Choi, Jae-Woo},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13975-13976},
  doi       = {10.1609/AAAI.V34I10.7260},
  url       = {https://mlanthology.org/aaai/2020/yildiz2020aaai-deep/}
}