Improving Sequential Query Recommendation with Immediate User Feedback. (arXiv:2205.06297v1 [cs.IR])

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 experimental study using log
files from a popular online literature discovery service and demonstrate that
our algorithm …

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