2 research outputs found
The Bitwise Hashing Trick for Personalized Search
Many real world problems require fast and efficient lexical comparison of
large numbers of short text strings. Search personalization is one such domain.
We introduce the use of feature bit vectors using the hashing trick for
improving relevance in personalized search and other personalization
applications. We present results of several lexical hashing and comparison
methods. These methods are applied to a user's historical behavior and are used
to predict future behavior. Using a single bit per dimension instead of
floating point results in an order of magnitude decrease in data structure
size, while preserving or even improving quality. We use real data to simulate
a search personalization task. A simple method for combining bit vectors
demonstrates an order of magnitude improvement in compute time on the task with
only a small decrease in accuracy
Embedding Compression with Isotropic Iterative Quantization
Continuous representation of words is a standard component in deep
learning-based NLP models. However, representing a large vocabulary requires
significant memory, which can cause problems, particularly on
resource-constrained platforms. Therefore, in this paper we propose an
isotropic iterative quantization (IIQ) approach for compressing embedding
vectors into binary ones, leveraging the iterative quantization technique well
established for image retrieval, while satisfying the desired isotropic
property of PMI based models. Experiments with pre-trained embeddings (i.e.,
GloVe and HDC) demonstrate a more than thirty-fold compression ratio with
comparable and sometimes even improved performance over the original
real-valued embedding vectors