Improving Sequential Query Recommendation with Immediate User Feedback. (arXiv:2205.06297v2 [cs.IR] UPDATED)

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. Due to the supervision involved in the learning process, such approaches
fail to adapt to immediate user feedback. 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 …

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