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Effective product recommendation using the real-time web

Author(s)
Garcia Esparza, Sandra  
O'Mahony, Michael P.  
Smyth, Barry  
Uri
http://hdl.handle.net/10197/2760
Date Issued
2010-12-14
Date Available
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
Springer
Copyright (Published Version)
Springer-Verlag London Limited 2011
Subjects

RTW

Real Time Web

Twitter

Blippr

Opinions

Recommendation scenar...

Subject – LCSH
Web 2.0
Recommender systems (Information filtering)
Online social networks
DOI
10.1007/978-0-85729-130-1_1
Web versions
http://dx.doi.org/10.1007/978-0-85729-130-1_1
Language
English
Status of Item
Peer reviewed
Journal
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
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
File(s)
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sgai-2010.pdf

Size

719.14 KB

Format

Adobe PDF

Checksum (MD5)

db6be5c77c290f48abe6fe71025a654b

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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