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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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