Retrieval & Interaction Machine for Tabular Data Prediction

@article{Qin2021RetrievalI,
  title={Retrieval \& Interaction Machine for Tabular Data Prediction},
  author={Jiarui Qin and Weinan Zhang and Rong Su and Zhirong Liu and Weiwen Liu and Ruiming Tang and Xiuqiang He and Yong Yu},
  journal={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
  year={2021}
}
  • Jiarui Qin, Weinan Zhang, +5 authors Yong Yu
  • Published 2021
  • Computer Science
  • Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc. Tabular data is structured into rows and columns, with each row as a data sample and each column as a feature attribute. Both the columns and rows of the tabular data carry useful patterns that could improve the model prediction performance. However, most existing models focus on the cross-column patterns yet overlook the cross-rowpatterns… Expand
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