Top-k maximum inner product search (MIPS) is a central task in many machine learning applications. This work extends top-k MIPS with a budgeted setting, that asks for the best approximate top-k MIPS given a limited budget of computational operations. We investigate recent advanced sampling algorithms, including wedge and diamond sampling, to solve budgeted top-k MIPS. First, we show that diamond sampling is essentially a combination of wedge sampling and basic sampling for top-k MIPS. Our theoretical analysis and empirical evaluation show that wedge is competitive (often superior) to diamond on approximating top-k MIPS regarding both efficiency and accuracy. Second, we propose dWedge, a very simple deterministic variant of wedge sampling for budgeted top-k MIPS. Empirically, dWedge provides significantly higher accuracy than other budgeted top-k MIPS solvers while maintaining a similar speedup.
CITATION STYLE
Lorenzen, S. S., & Pham, N. (2021). Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12457 LNAI, pp. 439–455). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-67658-2_25
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