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Discover Computing
Erschienen in: Discover Computing 2/2012

01.04.2012

Opinion-based entity ranking

verfasst von: Kavita Ganesan, ChengXiang Zhai

Erschienen in: Discover Computing | Ausgabe 2/2012

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Abstract

The deployment of Web 2.0 technologies has led to rapid growth of various opinions and reviews on the web, such as reviews on products and opinions about people. Such content can be very useful to help people find interesting entities like products, businesses and people based on their individual preferences or tradeoffs. Most existing work on leveraging opinionated content has focused on integrating and summarizing opinions on entities to help users better digest all the opinions. In this paper, we propose a different way of leveraging opinionated content, by directly ranking entities based on a user’s preferences. Our idea is to represent each entity with the text of all the reviews of that entity. Given a user’s keyword query that expresses the desired features of an entity, we can then rank all the candidate entities based on how well opinions on these entities match the user’s preferences. We study several methods for solving this problem, including both standard text retrieval models and some extensions of these models. Experiment results on ranking entities based on opinions in two different domains (hotels and cars) show that the proposed extensions are effective and lead to improvement of ranking accuracy over the standard text retrieval models for this task.
Vorheriger Artikel The optimum clustering framework: implementing the cluster hypothesis
Nächster Artikel On complexity reduction of concept lattices: three counterexamples

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Metadaten
Titel
Opinion-based entity ranking
verfasst von
Kavita Ganesan
ChengXiang Zhai
Publikationsdatum
01.04.2012
Verlag
Springer Netherlands
Erschienen in
Discover Computing / Ausgabe 2/2012
Print ISSN: 2948-2984
Elektronische ISSN: 2948-2992
DOI
https://doi.org/10.1007/s10791-011-9174-8