Budget Constrained Interactive Search for Multiple Targets
Xuliang Zhu, Xin Huang, Byron Choi, Jiaxin Jiang, Zhaonian Zou,, Jianliang Xu

TL;DR
This paper introduces a novel budget-constrained interactive graph search framework for efficiently identifying multiple targets within a hierarchy, addressing limitations of existing single-target methods.
Contribution
It formulates the kBM-IGS problem, designs a penalty-based closeness measure, and develops new algorithms for multiple target search under budget constraints.
Findings
Algorithms outperform baselines in accuracy and efficiency
Effective in large real-world datasets
Balances target probability and benefit gain
Abstract
Interactive graph search leverages human intelligence to categorize target labels in a hierarchy, which are useful for image classification, product categorization, and database search. However, many existing studies of interactive graph search aim at identifying a single target optimally, and suffer from the limitations of asking too many questions and not being able to handle multiple targets. To address these two limitations, in this paper, we study a new problem of budget constrained interactive graph search for multiple targets called kBM-IGS-problem. Specifically, given a set of multiple targets T in a hierarchy, and two parameters k and b, the goal is to identify a k-sized set of selections S such that the closeness between selections S and targets T is as small as possible, by asking at most a budget of b questions. We theoretically analyze the updating rules and design a…
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Taxonomy
TopicsOptimization and Search Problems · Mobile Crowdsensing and Crowdsourcing · Machine Learning and Algorithms
