On the real-time web as a source of recommendation knowledge

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Title: On the real-time web as a source of recommendation knowledge
Authors: Garcia Esparza, Sandra
O'Mahony, Michael P.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/2521
Date: Sep-2010
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 personalized commentary in real-time. Twitter is undoubtedly the king of the RTW. It boasts 100+ million users and generates in the region of 50m tweets per day. This RTW data is far from the structured data (ratings, product features, etc.) familiar to recommender systems research, but it is useful to consider its applicability to recommendation scenarios. In this short paper we describe an experiment to look at harnessing the real-time opinions of movie fans, expressed through the Twitter-like short textual reviews available on the Blippr service (www.blippr.com). In particular we describe how users and movies can be represented from the terms used in their associated reviews and describe a number of experiments to highlight the recommendation potential of this RTW data-source and approach.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2010 ACM
Keywords: AlgorithmsExperimentation
Subject LCSH: Recommender systems (Information filtering)
Social media
Blogs
DOI: 10.1145/1864708.1864773
Language: en
Status of Item: Peer reviewed
Is part of: RecSys'10 : proceedings of the 4th ACM Conference on Recommender Systems, Barcelona, Spain, September 26-30, 2010
Conference Details: Poster presented at the 4th ACM Conference on Recommender Systems (RecSys 2010), Barcelona, Spain, September 26-30, 2010
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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