Set Similarity Search Beyond MinHash

We consider the problem of approximate set similarity search under Braun-Blanquet similarity $B(\mathbf{x}, \mathbf{y}) = |\mathbf{x} \cap \mathbf{y}| / \max(|\mathbf{x}|, |\mathbf{y}|)$. The $(b_2, b_2)$-approximate Braun-Blanquet similarity search problem is to preprocess a collection of sets $P$ such that, given a query set $\mathbf{q}$, if there exists $\mathbf{x} \in P$ with $B(\mathbf{q}, \mathbf{x}) \geq b_1$, then we can efficiently return $\mathbf{x}' \in P$ with $B(\mathbf{q}, \mathbf{x}') > b_2$. We present a simple data structure that solves this problem with space usage $O(n^{1+ρ}\log n + \sum_{\mathbf{x} \in P}|\mathbf{x}|)$ and query time $O(|\mathbf{q}|n^ρ \log n)$ where $n = |P|$ and $ρ= \log(1/b_1)/\log(1/b_2)$. Making use of existing lower bounds for locality-sensitive hashing by O'Donnell et al. (TOCT 2014) we show that this value of $ρ$ is tight across the parameter space, i.e., for every choice of constants $0 < b_2 < b_1 < 1$. In the case where all sets have the same size our solution strictly improves upon the value of $ρ$ that can be obtained through the use of state-of-the-art data-independent techniques in the Indyk-Motwani locality-sensitive hashing framework (STOC 1998) such as Broder's MinHash (CCS 1997) for Jaccard similarity and Andoni et al.'s cross-polytope LSH (NIPS 2015) for cosine similarity. Surprisingly, even though our solution is data-independent, for a large part of the parameter space we outperform the currently best data-dependent method by Andoni and Razenshteyn (STOC 2015).