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12

AH
2006
Springer

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Ways of Computing Diverse Collaborative Recommendations

13 years 10 months ago

Ways of Computing Diverse Collaborative Recommendations

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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.

Derek G. Bridge, John Paul Kelly

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AH 2006 | Collaborative Recommenders | Conversational Recommender Systems | Inverse Pearson Correlation |

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Added	13 Jun 2010
Updated	13 Jun 2010
Type	Conference
Year	2006
Where	AH
Authors	Derek G. Bridge, John Paul Kelly

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