SAH: Shifting-aware Asymmetric Hashing For Reverse \(k\)-maximum Inner Product Search
Qiang Huang, Yanhao Wang, Anthony K. H. Tung . Arxiv 2022 – 0 citations
[Code]
[Paper]
This paper investigates a new yet challenging problem called Reverse (k)-Maximum Inner Product Search (R(k)MIPS). Given a query (item) vector, a set of item vectors, and a set of user vectors, the problem of R(k)MIPS aims to find a set of user vectors whose inner products with the query vector are one of the (k) largest among the query and item vectors. We propose the first subquadratic-time algorithm, i.e., Shifting-aware Asymmetric Hashing (SAH), to tackle the R(k)MIPS problem. To speed up the Maximum Inner Product Search (MIPS) on item vectors, we design a shifting-invariant asymmetric transformation and develop a novel sublinear-time Shifting-Aware Asymmetric Locality Sensitive Hashing (SA-ALSH) scheme. Furthermore, we devise a new blocking strategy based on the Cone-Tree to effectively prune user vectors (in a batch). We prove that SAH achieves a theoretical guarantee for solving the RMIPS problem. Experimental results on five real-world datasets show that SAH runs 4(\sim)8(\times) faster than the state-of-the-art methods for R(k)MIPS while achieving F1-scores of over 90%. The code is available at https://github.com/HuangQiang/SAH.