When Diversity Met Accuracy: A Story of Recommender Systems
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 experime...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-09-01
|
Series: | Proceedings |
Subjects: | |
Online Access: | http://www.mdpi.com/2504-3900/2/18/1178 |
id |
doaj-e27cb21de952460d83b512a693944abe |
---|---|
record_format |
Article |
spelling |
doaj-e27cb21de952460d83b512a693944abe2020-11-24T21:48:24ZengMDPI AGProceedings2504-39002018-09-01218117810.3390/proceedings2181178proceedings2181178When Diversity Met Accuracy: A Story of Recommender SystemsAlfonso Landin0Eva Suárez-García1Daniel Valcarce2Department of Computer Science, University of A Coruña, 15071 A Coruña, SpainDepartment of Computer Science, University of A Coruña, 15071 A Coruña, SpainDepartment of Computer Science, University of A Coruña, 15071 A Coruña, SpainDiversity 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.http://www.mdpi.com/2504-3900/2/18/1178recommender systemscollaborative filteringdiversitynovelty |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alfonso Landin Eva Suárez-García Daniel Valcarce |
spellingShingle |
Alfonso Landin Eva Suárez-García Daniel Valcarce When Diversity Met Accuracy: A Story of Recommender Systems Proceedings recommender systems collaborative filtering diversity novelty |
author_facet |
Alfonso Landin Eva Suárez-García Daniel Valcarce |
author_sort |
Alfonso Landin |
title |
When Diversity Met Accuracy: A Story of Recommender Systems |
title_short |
When Diversity Met Accuracy: A Story of Recommender Systems |
title_full |
When Diversity Met Accuracy: A Story of Recommender Systems |
title_fullStr |
When Diversity Met Accuracy: A Story of Recommender Systems |
title_full_unstemmed |
When Diversity Met Accuracy: A Story of Recommender Systems |
title_sort |
when diversity met accuracy: a story of recommender systems |
publisher |
MDPI AG |
series |
Proceedings |
issn |
2504-3900 |
publishDate |
2018-09-01 |
description |
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. |
topic |
recommender systems collaborative filtering diversity novelty |
url |
http://www.mdpi.com/2504-3900/2/18/1178 |
work_keys_str_mv |
AT alfonsolandin whendiversitymetaccuracyastoryofrecommendersystems AT evasuarezgarcia whendiversitymetaccuracyastoryofrecommendersystems AT danielvalcarce whendiversitymetaccuracyastoryofrecommendersystems |
_version_ |
1725892355294232576 |