Submodular maximization with multi-knapsack constraints and its applications in scientific literature recommendations

Yu, Qilian; Xu, Easton Li; Cui, Shuguang

doi:10.1109/globalsip.2016.7906050

Cited by 17 publications

(16 citation statements)

References 12 publications

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“…To handle various types of linear budget constraints in real-world problems, we adopt the general dknapsack constraint [22,23,26,38]. Specifically, a knapsack is defined by a cost function c : V → R + that assigns a positive cost to each element in the ground set V .…”

Section: Problem Formulationmentioning

confidence: 99%

“…KS needs a single pass over a stream and provides a 1−ε 1+d -approximate solution for SMDK. It improves the state-of-the-art approximation factor of 1 1+2d − ε for SMDK in append-only streams [38]. Then, we propose two novel frameworks, namely KNAPWINDOW (KW) and KNAPWIN-DOWPLUS (KW + ), for SMDK over sliding windows.…”

Section: Introductionmentioning

confidence: 99%

“…RSS is often formulated as selecting a subset of elements to maximize a utility function that quantifies the representativeness subject to some constraints. The utility functions are often chosen to be submodular to capture the "diminishing returns" property of representativeness [2,7,10,32,38,40], i.e., adding more elements decreases the marginal representativeness. A number of constraints are used to restrict the selected subset in various ways.…”

Section: Introductionmentioning

confidence: 99%

“…To restrict the summary size, two constraints are imposed: the number of selected posts in S, as well as their total length, is bounded. Additionally, the influence scores are also modeled as constraints so that more influential elements could be included in the summary [38].…”

Section: Introductionmentioning

confidence: 99%

“…As aforementioned, a number of RSS problems consider more general multi-knapsack (a.k.a. d-knapsack) constraints beyond cardinality [22,23,26,38]. However, the algorithms that only support cardinality constraints cannot provide solutions with any quality assurances in more general cases.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient Representative Subset Selection over Sliding Windows

Wang

Tan

2019

IEEE Trans. Knowl. Data Eng.

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Representative subset selection (RSS) is an important tool for users to draw insights from massive datasets. Existing literature models RSS as the submodular maximization problem to capture the "diminishing returns" property of the representativeness of selected subsets, but often only has a single constraint (e.g., cardinality), which limits its applications in many real-world problems. To capture the data recency issue and support different types of constraints, we formulate dynamic RSS in data streams as maximizing submodular functions subject to general d-knapsack constraints (SMDK) over sliding windows. We propose a KNAPWINDOW framework (KW) for SMDK. KW utilizes the KNAPSTREAM algorithm (KS) for SMDK in append-only streams as a subroutine. It maintains a sequence of checkpoints and KS instances over the sliding window. Theoretically, KW is 1−ε 1+d -approximate for SMDK. Furthermore, we propose a KNAPWINDOWPLUS framework (KW + ) to improve upon KW. KW + builds an index SUBKNAPCHK to manage the checkpoints and KS instances. SUBKNAPCHK deletes a checkpoint whenever it can be approximated by its successors. By keeping much fewer checkpoints, KW + achieves higher efficiency than KW while still guaranteeing a 1−ε ′ 2+2d -approximate solution for SMDK. Finally, we evaluate the efficiency and solution quality of KW and KW + in real-world datasets. The experimental results demonstrate that KW achieves more than two orders of magnitude speedups over the batch baseline and preserves high-quality solutions for SMDK over sliding windows. KW + further runs 5-10 times faster than KW while providing solutions with equivalent or even better utilities.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Representative Subset Selection over Sliding Windows

Wang

Tan

2019

IEEE Trans. Knowl. Data Eng.

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A Submodular Approach for Reference Recommendation

Kieu

Pham

Phan

et al. 2020

Communications in Computer and Information Science

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Choosing appropriate references for a given topic is an important, yet challenging task. The pool of potential candidates is typically very large, in the order of tens of thousands, and growing by the day. For this reason, this paper proposes an approach for automatically providing a reference list for a given abstract. The approach is based on an original submodular inference function which balances relevance, coverage and diversity in the reference list. Experiments are carried out using an ACL corpus as a source for the references and evaluated in terms of precision-recall, MAP and MRR. The results show the remarkable comparative performance of the proposed approach.

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Maximizing k-submodular functions under budget constraint: applications and streaming algorithms

Pham

Vu²,

et al. 2022

J Comb Optim

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Submodular maximization with multi-knapsack constraints and its applications in scientific literature recommendations

Cited by 17 publications

References 12 publications

Efficient Representative Subset Selection over Sliding Windows

Efficient Representative Subset Selection over Sliding Windows

A Submodular Approach for Reference Recommendation

Maximizing k-submodular functions under budget constraint: applications and streaming algorithms

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