A Bandit Approach to Maximum Inner Product Search

Rui Liu, Tianyi Wu, Barzan Mozafari

Abstract:

There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search
(MIPS). To achieve fast query time, state-of-the-art techniques require significant preprocessing, which can be a burden when the number of subsequent queries is not sufficiently
large to amortize the cost. Furthermore, existing methods do
not have the ability to directly control the suboptimality of
their approximate results with theoretical guarantees. In this
paper, we propose the first approximate algorithm for MIPS
that does not require any preprocessing, and allows users to
control and bound the suboptimality of the results. We cast
MIPS as a Best Arm Identification problem, and introduce a
new bandit setting that can fully exploit the special structure
of MIPS. Our approach outperforms state-of-the-art methods
on both synthetic and real-world datasets.