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Enhancing the diversity of conversational collaborative recommendations: a comparison

Artificial Intelligence Review volume 25, pages 79–95 (2006)Cite this article

Abstract

In conversational collaborative recommender systems, user feedback influences the recommendations. We report mechanisms for enhancing the diversity of the recommendations made by collaborative recommenders. We focus on techniques for increasing diversity that rely on collaborative data only. In our experiments, we compare different mechanisms and show that, if recommendations are diverse, users find target items in many fewer recommendation cycles.

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Authors and Affiliations

  1. Department of Computer Science, University College Cork, Western Road, Cork, Ireland

    John Paul Kelly & Derek Bridge

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  1. John Paul Kelly
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  2. Derek Bridge
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Correspondence to John Paul Kelly.

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Kelly, J.P., Bridge, D. Enhancing the diversity of conversational collaborative recommendations: a comparison. Artif Intell Rev 25, 79–95 (2006). https://doi.org/10.1007/s10462-007-9023-8

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  • DOI: https://doi.org/10.1007/s10462-007-9023-8

Keywords

  • Recommender systems
  • Collaborative recommendations
  • Diversity