Unsupervised Multi-Index Semantic Hashing

TL;DR

This paper introduces MISH, an unsupervised semantic hashing model optimized for multi-index hashing, achieving faster search times while maintaining high effectiveness in document similarity retrieval.

Contribution

MISH is a novel unsupervised hashing approach with training objectives that optimize hash codes for multi-index hashing, enhancing search efficiency without sacrificing accuracy.

Findings

MISH outperforms state-of-the-art baselines in speed by over 33%.

MISH maintains state-of-the-art effectiveness in document similarity search.

The training objectives are model-agnostic and easily integrable into existing models.

Abstract

Semantic hashing represents documents as compact binary vectors (hash codes) and allows both efficient and effective similarity search in large-scale information retrieval. The state of the art has primarily focused on learning hash codes that improve similarity search effectiveness, while assuming a brute-force linear scan strategy for searching over all the hash codes, even though much faster alternatives exist. One such alternative is multi-index hashing, an approach that constructs a smaller candidate set to search over, which depending on the distribution of the hash codes can lead to sub-linear search time. In this work, we propose Multi-Index Semantic Hashing (MISH), an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing. We derive novel training objectives, which enable to learn hash codes that reduce the candidate sets produced by multi-index hashing, while being end-to-end trainable. In fact, our proposed training objectives are model agnostic, i.e., not tied to how the hash codes are generated specifically in MISH, and are straight-forward to include in existing and future semantic hashing models. We experimentally compare MISH to state-of-the-art semantic hashing baselines in the task of document similarity search. We find that even though multi-index hashing also improves the efficiency of the baselines compared to a linear scan, they are still upwards of 33% slower than MISH, while MISH is still able to obtain state-of-the-art effectiveness.