Abstract
Conversational recommender systems adapt the sets of products they recommend in light of user feedback. Our contribution here is to devise and compare four different mechanisms for enhancing the diversity of the recommendations made by collaborative recommenders. Significantly, we increase diversity using collaborative data only. We find that measuring the distance between products using Hamming Distance is more effective than using Inverse Pearson Correlation.
This material is based on works supported by Science Foundation Ireland under Grant No. 03/IN.3/136. We are grateful to Professor Barry Smyth for his advice and to the GroupLens project team for making their data available.
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Bridge, D., Kelly, J.P. (2006). Ways of Computing Diverse Collaborative Recommendations. In: Wade, V.P., Ashman, H., Smyth, B. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2006. Lecture Notes in Computer Science, vol 4018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11768012_6
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DOI: https://doi.org/10.1007/11768012_6
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