From Opinions to Recommendations

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Title: From Opinions to Recommendations
Authors: O'Mahony, Michael P.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/10292
Date: 3-May-2018
Online since: 2019-05-07T07:44:40Z
Abstract: Traditionally, recommender systems have relied on user preference data (such as ratings) and product descriptions (such as meta-data) as primary sources of recommendation knowledge. More recently, new sources of recommendation knowledge in the form of social media information and other kinds of user-generated content have emerged as viable alternatives. For example, services such as Twitter, Facebook, Amazon and TripAdvisor provide a rich source of user opinions, positive and negative, about a multitude of products and services. They have the potential to provide recommender systems with access to the fine-grained opinions of real users based on real experiences. This chapter will explore how product opinions can be mined from such sources and can be used as the basis for recommendation tasks. We will draw on a number of concrete case-studies to provide different examples of how opinions can be extracted and used in practice.
Funding Details: Science Foundation Ireland
Type of material: Book Chapter
Publisher: Springer
Series/Report no.: Lecture Notes in Computer Science book series (LNCS, volume 10100)
Copyright (published version): 2018 the Authors
Keywords: Recommender systemsOpinion miningSentiment analysis
DOI: 10.1007/978-3-319-90092-6_13
Language: en
Status of Item: Peer reviewed
Is part of: Brusilovsky P., He, D. (eds.). Social Information Access - Systems and Technologies
ISBN: 978-3-319-90091-9
Appears in Collections:Insight Research Collection

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