NDLI: Selecting a comprehensive set of reviews

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Selecting a comprehensive set of reviews

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Selecting a comprehensive set of reviews

Content Provider	ACM Digital Library
Author	Terzi, Evimaria Tsaparas, Panayiotis Ntoulas, Alexandros
Abstract	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.
Starting Page	168
Ending Page	176
Page Count	9
File Format	PDF
ISBN	9781450308137
DOI	10.1145/2020408.2020440
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-08-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Greedy algorithms Set cover Review selection
Content Type	Text
Resource Type	Article

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