@incollection{fdi:010083557,
  title = {{D}ora the explorer : exploring very large data with interactive deep reinforcement learning authors' copy [demonstration paper]},
  author = {{P}ersonnaz, {A}. and {A}mer-{Y}ahia, {S}. and {B}erti-{E}quille, {L}aure and {F}abricius, {M}. and {S}ubramanian, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{W}e demonstrate dora the explorer, a system that guides users in finding items of interest in a very large data set. dora the explorer provides users with the full spectrum of exploration modes and is driven by {D}ata {F}amiliarity or {C}uriosity, as well as {U}ser {I}nterventions. dora the explorer is able to handle data and search scenario complexity, i.e., the difficulty to find scattered/clustered individual records in the data set, and user ability to express what s/he needs. dora the explorer relies on {D}eep {R}einforcement {L}earning that combines intrinsic (curiosity) and extrinsic (familiarity) rewards. dora's main goal is to support scientific discovery from data. {W}e describe the system architecture and illustrate it with three demonstration scenarios on a 2.6 million galaxies {SDSS}, a large sky survey data set1. {A} video of dora the explorer is available at https://bit.ly/dora-demo, the code https://github.com/apersonnaz/rl-guided-galaxy-exploration, and the application at https://bit.ly/dora-application.},
  keywords = {},
  booktitle = {{CIKM}'21 : proceedings of the 30th {ACM} {I}nternational {C}onference on {I}nformation and {K}nowledge {M}anagement},
  numero = {},
  pages = {4769--4773},
  address = {{N}ew {Y}ork},
  publisher = {{ACM}},
  series = {},
  year = {2021},
  DOI = {10.1145/3459637.3481967},
  ISBN = {978-1-4503-8446-9},
  URL = {https://www.documentation.ird.fr/hor/fdi:010083557},
}