Categorical Diversity-Aware Inner Product Search

Hirata, Kohei; Amagata, Daichi; Fujita, Sumio et al.

IEEE Access, 2023, 11, 2586-2596

Number of Access:722（2025-10-07 04:03 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/92776

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Title
Categorical Diversity-Aware Inner Product Search
Creator
CreatorName
Hirata, Kohei
NRID
1000040770649 1000040770649
researchmap permalink
amgt amgt
Scopus Author ID
55811167300 55811167300
CreatorName
Amagata, Daichi
CreatorName
Fujita, Sumio
NRID
1000020294043 1000020294043
ORCID
0000-0003-4807-3156 0000-0003-4807-3156
researchmap permalink
hara-osakau hara-osakau
Scopus Author ID
7404213547 7404213547
CreatorName
Hara, Takahiro
Subject
category
diversification
high-dimensional data
Inner product search
Description
Hirata K., Amagata D., Fujita S., et al. Categorical Diversity-Aware Inner Product Search. IEEE Access 11, 2586 (2023); https://doi.org/10.1109/ACCESS.2023.3234072.
Abstract
The problem of maximum inner product search (MIPS) is one of the most important components in machine learning systems. However, this problem does not care about diversity, although result diversification can improve user satisfaction. This paper hence considers a new problem, namely the categorical diversity-aware IPS problem, in which users can select preferable categories. Exactly solving this problem needs O(n) time, where n is the number of vectors, and is not efficient for large n. We hence propose an approximation algorithm that has a probabilistic success guarantee and runs in sub-linear time to n. We conduct extensive experiments on real datasets, and the results demonstrate the superior performance of our algorithm to that of a baseline using an existing MIPS technique.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Source Title
IEEE Access
Volume (Issue)
11
Page
2586-2596
Date of Issued
2023-01-01
Language
English
Handle URL
https://hdl.handle.net/11094/92776
eISSN
21693536
Relation.isVersionOf
DOI (Publisher Version)
https://doi.org/10.1109/ACCESS.2023.3234072
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article