Research Notes

[Paper Review] Reciptor: An Effective Pretrained Model for Recipe Representation Learning 본문

Paper Review

[Paper Review] Reciptor: An Effective Pretrained Model for Recipe Representation Learning

jiachoi 2022. 8. 30. 00:42

Title: Reciptor: An Effective Pretrained Model for Recipe Representation Learning

Authors: Diya Li, Mohammed J. Zaki

Summary

  • a joint approach for learning effective pretrained recipe embeddings using both the ingredients and cooking instructions
  • a novel set transformer-based joint model to learn recipe representations that preserve permutation-invariance

 

Framework 

  1. Tags data: Model Validation에 사용하는 데이터, Recipe의 Label, Node Classification으로 검증 시 활용
  2. Ingredients data: Set Transformer로 embedding, input의 순서에 상관없이 학습시키기 위함
  3. Instructions: LSTM으로 embedding, instruction의 sequence를 학습시키기 위함

Paper URL: https://dl.acm.org/doi/10.1145/3394486.3403223

 

RECIPTOR: An Effective Pretrained Model for Recipe Representation Learning | Proceedings of the 26th ACM SIGKDD International Co

ABSTRACT Recipe representation plays an important role in food computing for perception, recognition, recommendation and other applications. Learning pretrained recipe embeddings is a challenging task, as there is a lack of high quality annotated food data

dl.acm.org

 

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