Set similarity search is a problem of central interest to a wide variety of applications such as data cleaning and web search. Past approaches on set similarity search utilize either heavy indexing structures, incurring large search costs or indexes that produce large candidate sets. In this paper, we design a learning-based exact set similarity search approach, LES3. Our approach first partitions sets into groups, and then utilizes a light-weight bitmap-like indexing structure, called token-group matrix (TGM), to organize groups and prune out candidates given a query set. In order to optimize pruning using the TGM, we analytically investigate the optimal partitioning strategy under certain distributional assumptions. Using these results, we then design a learning-based partitioning approach called L2P and an associated data representation encoding, PTR, to identify the partitions. We conduct extensive experiments on real and synthetic datasets to fully study LES3, establishing the effectiveness and superiority over other applicable approaches.

翻译：相近搜索是数据清理和网络搜索等多种应用中心关注的一个问题。 以往的相近搜索方法使用重指数结构、 导致大量搜索成本或产生大型候选数据集的索引。 在本文中,我们设计了基于学习的精确相似搜索方法,即LES3。 我们的第一个分区将组合成一组,然后使用轻量位图相似的索引结构,称为象征性群体矩阵(TGM)来组织群体和对被查询的候选人进行筛选。 为了优化利用TGM, 我们根据某些分配假设对最佳分割战略进行了分析研究。 然后,我们利用这些结果设计了一个称为L2P的基于学习的分割方法和一个相关的数据表示编码,即PTR,以确定分区。 我们对真实的和合成的数据集进行了广泛的实验,以充分研究LES3, 确定相对于其他适用方法的有效性和优越性。