学術雑誌論文

学術雑誌論文

text

IEEE Access. 2023, 11, p. 2586-2596

Categorical Diversity-Aware Inner Product Search

Institute of Electrical and Electronics Engineers Inc.

英語

text

2023-01-01

https://doi.org/10.1109/ACCESS.2023.3234072

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.

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.

This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

92776

Categorical Diversity-Aware Inner Product Search

Institute of Electrical and Electronics Engineers Inc.

英語

2023-01-01

IEEE Access

11

2586

2596

https://doi.org/10.1109/ACCESS.2023.3234072

https://hdl.handle.net/11094/92776