  In recommendation systems, the relevance and novelty of the final results are
selected through a cascade system of Matching -> Ranking -> Strategy. The
matching model serves as the starting point of the pipeline and determines the
upper bound of the subsequent stages. Balancing the relevance and novelty of
matching results is a crucial step in the design and optimization of
recommendation systems, contributing significantly to improving recommendation
quality. However, the typical matching algorithms have not simultaneously
addressed the relevance and novelty perfectly. One main reason is that deep
matching algorithms exhibit significant uncertainty when estimating items in
the long tail (e.g., due to insufficient training samples) items.The
uncertainty not only affects the training of the models but also influences the
confidence in the index construction and beam search retrieval process of these
models. This paper proposes the UICR (Uncertainty-based explore for Index
Construction and Retrieval) algorithm, which introduces the concept of
uncertainty modeling in the matching stage and achieves multi-task modeling of
model uncertainty and index uncertainty. The final matching results are
obtained by combining the relevance score and uncertainty score infered by the
model. Experimental results demonstrate that the UICR improves novelty without
sacrificing relevance on realworld industrial productive environments and
multiple open-source datasets. Remarkably, online A/B test results of display
advertising in Shopee demonstrates the effectiveness of the proposed algorithm.
