b-Bit Minwise Hashing for Estimating Three-Way Similarities

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Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search (2014, methodology): “CRS naturally extends to multi-way similarity (Li et al., 2010; Shrivastava & Li, 2013) and the method is not restricted to binary data.”
Theory and applications of b-bit minwise hashing (2011, methodology): “In [21], we extended b-bit minwise hashing to estimate 3-way resemblance.”
Hashing for Similarity Search: A Survey (2014, background): “B-bit minwise hashing [71], [70] only uses the lowest b-bits of the min-hash value as a short hash value, which gains substantial advantages in terms of storage space while still leading to an unbiased estimator of the resemblance (the Jaccard coefficient).”
Learning to Hash for Indexing Big Data—A Survey (2015, methodology): “By converting rank lists to a triplet tensor matrix, listwise hashing is designed to preserve the ranking in the Hamming space [76].”
Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search (2013, background): “Our work exploits the fact that resemblance can be naturally extended to k-way resemblance similarity [18, 21], defined over k sets {S1, S2, .”