Content and Structure Coverage: Extracting a Diverse Information Subset

Baojun Ma (), Qiang Wei (), Guoqing Chen (), Jin Zhang () and Xunhua Guo ()
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Baojun Ma: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Qiang Wei: Research Center for Contemporary Management, School of Economics and Management, Tsinghua University, Beijing 100084, China
Guoqing Chen: Research Center for Contemporary Management, School of Economics and Management, Tsinghua University, Beijing 100084, China
Jin Zhang: School of Business, Renmin University of China, Beijing 100872, China
Xunhua Guo: Research Center for Contemporary Management, School of Economics and Management, Tsinghua University, Beijing 100084, China

INFORMS Journal on Computing, 2017, vol. 29, issue 4, 660-675

Abstract: Recent years have witnessed a rapid increase in online data volume and the growing challenge of information overload for web use and applications. Thus, information diversity is of great importance to both information service providers and users of search services. Based on a diversity evaluation measure (namely, information coverage), a heuristic method— FastCov C+S -Select —with corresponding algorithms is designed on the greedy submodular idea. First, we devise the Cov C+S -Select algorithm, which possesses the characteristic of asymptotic optimality, to optimize information coverage using a strategy in the spirit of simulated annealing. To accelerate the efficiency of Cov C+S -Select , its fast approximation (i.e., FastCov C+S -Select ) is then developed through a heuristic strategy to downsize the solution space with the properties of information coverage. Furthermore, ample experiments have been conducted to show the effectiveness, efficiency, and parameter robustness of the proposed method, along with comparative analyses revealing the performance’s advantages over other related methods.

Keywords: information diversity; information content coverage; information structure coverage; submodularity; fast approximation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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