Non-Linear Multiple Field Interactions Neural Document Ranking

Kentaro Takiguchi, Niall Twomey, Luis M. Vaquero

Research output: Contribution to journalArticle (Academic Journal)

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Ranking tasks are usually based on the text of the main body of the page and the actions (clicks) of users on the page. There are other elements that could be leveraged to better contextualise the ranking experience (e.g. text in other fields, query made by the user, images, etc). We present one of the first in-depth analyses of field interaction for multiple field ranking in two separate datasets. While some works have taken advantage of full document structure, some aspects remain unexplored. In this work we build on previous analyses to show how query-field interactions, non-linear field interactions, and the architecture of the underlying neural model affect performance.
Original languageEnglish
Number of pages7
Publication statusPublished - 18 Nov 2020


  • recipes
  • neural networks
  • field interactions
  • query-field
  • first-order
  • document ranking


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