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Ranking with submodular functions on a budget. (arXiv:2204.04168v1 [cs.DS])
April 11, 2022, 1:11 a.m. | Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
cs.LG updates on arXiv.org arxiv.org
Submodular maximization has been the backbone of many important
machine-learning problems, and has applications to viral marketing,
diversification, sensor placement, and more. However, the study of maximizing
submodular functions has mainly been restricted in the context of selecting a
set of items. On the other hand, many real-world applications require a
solution that is a ranking over a set of items. The problem of ranking in the
context of submodular function maximization has been considered before, but to
a much …
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