Content and Structure Coverage: Extracting a Diverse Information Subset
针对网络信息过载问题，基于信息覆盖多样性度量，提出了一种贪心子模启发式方法FastCov C+S-Select，通过模拟退火和启发式策略优化信息覆盖，实验证明其有效性、效率和参数鲁棒性。
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. The online appendix is available at https://doi.org/10.1287/ijoc.2017.0753 .