Selecting a comprehensive set of reviews
Selecting a comprehensive set of reviews
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DOI：
10.1145/2020408.2020440
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
Panayiotis Tsaparas;A. Ntoulas;Evimaria Terzi
中科院分区：
文献类型：
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作者：
Panayiotis Tsaparas;A. Ntoulas;Evimaria Terzi
从娱乐、购物到医疗服务，在线用户评论在用户各种任务的决策过程中发挥着核心作用。随着用户生成的评论的激增，有一种机制来帮助用户（信息消费者）处理信息过载，并向他们提供满足其信息需求的小型综合评论集变得至关重要。这对手机用户来说尤其重要，因为他们需要快速做出决定，而且他们的设备显示评论的屏幕空间有限。以前的方法通过根据评论的（估计的）有用性对评论进行排名来解决这个问题。然而，这样的方法并没有考虑到这样一个事实，即前几个高质量的评论可能是高度冗余的，重复相同的信息，或者呈现相同的积极（或消极）观点。在这项工作中，我们关注的问题是选择覆盖被评审项目的许多不同方面的少数高质量评审的综合集。我们将这个问题表述为一个最大覆盖问题，并且我们提出了一个通用的形式，可以对评审集选择的不同变体进行建模。我们描述了我们考虑的不同变量的算法，并且，只要可能，我们提供了关于最优解的近似保证。我们还对真实数据进行了实验评估，以了解覆盖对用户的价值。
Online user reviews play a central role in the decision-making process of users for a variety of tasks, ranging from entertainment and shopping to medical services. As user-generated reviews proliferate, it becomes critical to have a mechanism for helping the users (information consumers) deal with the information overload, and presenting them with a small comprehensive set of reviews that satisfies their information need. This is particularly important for mobile phone users, who need to make decisions quickly, and have a device with limited screen real-estate for displaying the reviews. Previous approaches have addressed the problem by ranking reviews according to their (estimated) helpfulness. However, such approaches do not account for the fact that the top few high-quality reviews may be highly redundant, repeating the same information, or presenting the same positive (or negative) perspective. In this work, we focus on the problem of selecting a comprehensive set of few high-quality reviews that cover many different aspects of the reviewed item. We formulate the problem as a maximum coverage problem, and we present a generic formalism that can model the different variants of review-set selection. We describe algorithms for the different variants we consider, and, whenever possible, we provide approximation guarantees with respect to the optimal solution. We also perform an experimental evaluation on real data in order to understand the value of coverage for users.