Effective Product Recommendation using the Real-Time Web

  • Sandra Garcia Esparza
  • Michael P. O’Mahony
  • Barry Smyth
Conference paper


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 day1. 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 ( 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.


Recommender System Sentiment Analysis Target User Similar User Recommendation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Based on work supported by Science Foundation Ireland, Grant No. 07/CE/I1147.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.CLARITY: Centre for Sensor Web Technologies, School of Computer Science and InformaticsUniversity College DublinDublinIreland

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