A Greedy Approach for Budgeted Maximum Inner Product Search

Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit Dhillon

Abstract:   Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of a low-rank matrix factorization model for a recommender system. There have been some works on how to perform MIPS in sub-linear time recently. However, most of them do not have the flexibility to control the trade-off between search efficient and search quality. In this paper, we study the MIPS problem with a computational budget. By carefully studying the problem structure of MIPS, we develop a novel Greedy-MIPS algorithm, which can handle budgeted MIPS by design. While simple and intuitive, Greedy-MIPS yields surprisingly superior performance compared to state-of-the-art approaches. As a specific example, on a candidate set containing half a million vectors of dimension 200, Greedy-MIPS runs 200x faster than the naive approach while yielding search results with the top-5 precision greater than 75%.

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  • A Greedy Approach for Budgeted Maximum Inner Product Search (pdf)
    H. Yu, C. Hsieh, Q. Lei, I. Dhillon.
    In Neural Information Processing Systems (NIPS), December 2017.