Effective product recommendation using the real-time web

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Title: Effective product recommendation using the real-time web
Authors: Garcia Esparza, Sandra
O'Mahony, Michael P.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/2760
Date: 14-Dec-2010
Online since: 2011-02-15T12:57:35Z
Abstract: The so-called real-time web (RTW) is a web of opinions, comments, and personal viewpoints, often expressed in the form of short, 140-character text messages providing abbreviated and highly personalized commentary in real-time. Today, Twitter is undoubtedly the king of the RTW. It boasts 190 million users and generates in the region of 65m tweets per day. This RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research but it is useful to consider its applicability to recommendation scenarios. In this paper we consider harnessing the real-time opinions of users, expressed through the Twitter-like short textual reviews available on the Blippr service (www.blippr.com). In particular we describe how users and products can be represented from the terms used in their associated reviews and describe experiments to highlight the recommendation potential of this RTW data-source and approach.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): Springer-Verlag London Limited 2011
Keywords: RTWReal Time WebTwitterBlipprOpinionsRecommendation scenarios
Subject LCSH: Web 2.0
Recommender systems (Information filtering)
Online social networks
DOI: 10.1007/978-0-85729-130-1_1
Other versions: http://dx.doi.org/10.1007/978-0-85729-130-1_1
Language: en
Status of Item: Peer reviewed
Is part of: Bramer, M., Petridis, M. and Hopgood, A. (eds.). Research and Development in Intelligent Systems XXVII : Incorporating Applications and Innovations in Intelligent Systems XVIII : Proceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
Conference Details: Paper presented at the Thirtieth SGAI International Conference on Artificial Intelligence (AI-2010), 14-16 December 2010, Cambridge, England, UK
ISBN: 978-0-85729-129-5
Appears in Collections:Computer Science Research Collection

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