Providing Consistent Opinions from Online Reviews: A Heuristic Stepwise Optimization Approach

Providing Consistent Opinions from Online Reviews: A Heuristic Stepwise Optimization Approach
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从在线评论中提供一致的意见:启发式逐步优化方法

DOI:
10.1287/ijoc.2015.0672
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发表时间:
2016-03
影响因子:
2.1
通讯作者:
卫强
卫强
中科院分区:
计算机科学3区
文献类型:
--
作者:
张遵强;陈国青;张瑾;郭迅华;卫强

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评论摘要和评论排行榜之间的一致性对于消费者来说很重要,这样他们才能在购买决策中有效和高效地利用在线评论。本文研究了这一一致性问题,并将其描述为一个优化问题。基于消费者的阅读行为,考虑了消费者从排行榜上阅读的所有可能的评论集,目标是最大化预期的一致性。由于问题的NP难性,寻找最优排序表的精确方法在实践中通常是不能接受的。为此,提出了一种启发式方法(增强型逐步优化方法)。这种方法是一种有效和高效的近似方法,它根据预期的一致性值、优势和执行时间迭代地选择评论添加到排名列表中。在不同的环境和环境下,对合成数据和真实数据进行了密集的实验,并进行了相关的用户研究。
The consistency between review summaries and review ranking lists is important for consumers so they can utilize online reviews effectively and efficiently in their purchase decisions. This paper examines this consistency issue and formulates it as an optimization problem. Based on consumers’ reading behaviors, all possible sets of reviews that consumers would read from ranking lists are considered; the objective is to maximize the expected consistency. Because of the NP-hardness of the problem, exact methods that search for the optimal ranking lists are generally not acceptable in practice. Hence, a heuristic approach (the enhanced stepwise optimization procedure) is proposed. This approach is an effective and efficient approximation that selects reviews iteratively to add to the ranking lists in light of expected consistency value, superiority, and execution time. Intensive experiments on both synthetic and real data are conducted, with various environments and settings, along with a relevant user study...
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