# Unsupervised Semantic Hashing with Pairwise Reconstruction

arXiv: https://arxiv.org/abs/2007.00380 · HTML: https://mlsanity.com/paper/2007.00380/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Unsupervised Multi-Index Semantic Hashing](https://mlsanity.com/paper/2103.14460/) (2021)
- [Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering](https://mlsanity.com/paper/2103.14455/) (2021)
- [Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval](https://mlsanity.com/paper/2105.13066/) (2021)
- [Representation Learning for Efficient and Effective Similarity Search and Recommendation](https://mlsanity.com/paper/2109.01815/) (2021)
- [Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading](https://mlsanity.com/paper/2109.06736/) (2021)

## Abstract (arXiv)

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.