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Proceedings 2018, 2(18), 1178;

When Diversity Met Accuracy: A Story of Recommender Systems

Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain
Presented at the XoveTIC Congress, A Coruña, Spain, 27–28 September 2018.
Author to whom correspondence should be addressed.
Published: 14 September 2018
PDF [234 KB, uploaded 14 September 2018]


Diversity and accuracy are frequently considered as two irreconcilable goals in the field of Recommender Systems. In this paper, we study different approaches to recommendation, based on collaborative filtering, which intend to improve both sides of this trade-off. We performed a battery of experiments measuring precision, diversity and novelty on different algorithms. We show that some of these approaches are able to improve the results in all the metrics with respect to classical collaborative filtering algorithms, proving to be both more accurate and more diverse. Moreover, we show how some of these techniques can be tuned easily to favour one side of this trade-off over the other, based on user desires or business objectives, by simply adjusting some of their parameters.
Keywords: recommender systems; collaborative filtering; diversity; novelty recommender systems; collaborative filtering; diversity; novelty
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Landin, A.; Suárez-García, E.; Valcarce, D. When Diversity Met Accuracy: A Story of Recommender Systems. Proceedings 2018, 2, 1178.

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