# Unsupervised Semantic Hashing with Pairwise Reconstruction Source: [Unsupervised Semantic Hashing with Pairwise Reconstruction]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:43.424Z Authors: Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma Published: 2020-07-01T10:54:27.000Z ## Abstract Semantic Hashing is a popular family of methods for efficient similarity search in large-scale datasets. In Semantic Hashing, documents are encoded as short binary vectors (i.e., hash codes), such that semantic similarity can be efficiently computed using the Hamming distance. Recent state-of-the-art approaches have utilized weak supervision to train better performing hashing models. Inspired by this, we present Semantic Hashing with Pairwise Reconstruction (PairRec), which is a discrete variational autoencoder based hashing model. PairRec first encodes weakly supervised training pairs (a query document and a semantically similar document) into two hash codes, and then learns to reconstruct the same query document from both of these hash codes (i.e., pairwise reconstruction). This pairwise reconstruction enables our model to encode local neighbourhood structures within the hash code directly through the decoder. We experimentally compare PairRec to traditional and state-of-the-art approaches, and obtain significant performance improvements in the task of document similarity search. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Hashing on TMC (test)](): Semantic Hashing; TMC (test); Precision: 0.8303. - [Semantic Hashing on Reuters (test)](): Semantic Hashing; Reuters (test); Precision: 0.8468. ## Original sources and implementation [Original paper]() [PDF]()