Projective Clustering Product Quantization

Krishnan, Aditya; Liberty, Edo

doi:10.48550/arxiv.2112.02179

Cited by 1 publication

(1 citation statement)

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“…Our method is evaluated on three real datasets, LastFM, Glove, and Music100, which were used in the evaluation of multiple MIPS algorithms (Morozov and Babenko 2018;Guo et al 2020;Krishnan and Liberty 2021;Zhang et al 2022). The dataset splitting and experimental results on other datasets, including EchoNest and Yahoo!Music, are attached in the Appendix.…”

Section: Datasetsmentioning

confidence: 99%

Query-Aware Quantization for Maximum Inner Product Search

Zhang

Lian

ZHANG

et al. 2023

AAAI

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Maximum Inner Product Search (MIPS) plays an essential role in many applications ranging from information retrieval, recommender systems to natural language processing. However, exhaustive MIPS is often expensive and impractical when there are a large number of candidate items. The state-of-the-art quantization method of approximated MIPS is product quantization with a score-aware loss, developed by assuming that queries are uniformly distributed in the unit sphere. However, in real-world datasets, the above assumption about queries does not necessarily hold. To this end, we propose a quantization method based on the distribution of queries combined with sampled softmax. Further, we introduce a general framework encompassing the proposed method and multiple quantization methods, and we develop an effective optimization for the proposed general framework. The proposed method is evaluated on three real-world datasets. The experimental results show that it outperforms the state-of-the-art baselines.

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Section: Datasetsmentioning

confidence: 99%