Quantization based Fast Inner Product Search
This addresses the need for efficient similarity search in high-dimensional data, such as in recommendation systems or deep learning applications, with incremental improvements over prior methods.
The paper tackles the problem of fast approximate Maximum Inner Product Search (MIPS) by proposing a quantization-based approach that minimizes inner product quantization error, and it significantly outperforms existing state-of-the-art methods on various datasets, including those from deep neural networks.
We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS). Each database vector is quantized in multiple subspaces via a set of codebooks, learned directly by minimizing the inner product quantization error. Then, the inner product of a query to a database vector is approximated as the sum of inner products with the subspace quantizers. Different from recently proposed LSH approaches to MIPS, the database vectors and queries do not need to be augmented in a higher dimensional feature space. We also provide a theoretical analysis of the proposed approach, consisting of the concentration results under mild assumptions. Furthermore, if a small sample of example queries is given at the training time, we propose a modified codebook learning procedure which further improves the accuracy. Experimental results on a variety of datasets including those arising from deep neural networks show that the proposed approach significantly outperforms the existing state-of-the-art.