Improving Sequential Query Recommendation with Immediate User Feedback

@article{Parambath2022ImprovingSQ,
  title={Improving Sequential Query Recommendation with Immediate User Feedback},
  author={Shameem Puthiya Parambath and Christos Anagnostopoulos and Roderick Murray-Smith},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.06297}
}
We propose an algorithm for next query recommendation in interactive data exploration settings, like knowledge discovery for information gathering. The state-of-the-art query recommendation algorithms are based on sequence-to-sequence learning approaches that exploit historical interaction data. We propose to augment the transformer-based causal language models for query recommendations to adapt to the immediate user feedback using multi-armed bandit (MAB) framework. We conduct a large-scale… 
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