On the real-time web as a source of recommendation knowledge
|Title:||On the real-time web as a source of recommendation knowledge||Authors:||Garcia Esparza, Sandra
O'Mahony, Michael P.
|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:||Algorithms; Experimentation||Subject LCSH:||Recommender systems (Information filtering)
|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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