A Conversational Collaborative Filtering Approach to Recommendation

  • Eoin Hurrell
  • Alan F. Smeaton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8237)


Recent work has shown the value of treating recommendation as a conversation between user and system, which conversational recommenders have done by allowing feedback like “not as expensive as this” on recommendations. This allows a more natural alternative to content-based information access. Our research focuses on creating a viable conversational methodology for collaborative-filtering recommendation which can apply to any kind of information, especially visual. Since collaborative filtering does not have an intrinsic understanding of the items it suggests, i.e. it doesn’t understand the content, it has no obvious mechanism for conversation. Here we develop a means by which a recommender driven purely by collaborative filtering can sustain a conversation with a user and in our evaluation we show that it enables finding multimedia items that the user wants without requiring domain knowledge.


Information Retrieval Recommender System Recommendation Algorithm Average Prediction Error Good Recommendation 
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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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Eoin Hurrell
    • 1
  • Alan F. Smeaton
    • 1
    • 2
  1. 1.CLARITY: Centre for Sensor Web Technologies and School of ComputingDublin City UniversityDublin 9Ireland
  2. 2.INSIGHT: Big Data and Analytics Research Centre and School of ComputingDublin City UniversityDublin 9Ireland

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