Mutual proximity graphs for improved reachability in music recommendation.
Mutual proximity graphs for improved reachability in music recommendation.
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DOI：
10.1080/09298215.2017.1354891
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发表时间：
2018
影响因子：
1.1
通讯作者：
Stevens J
中科院分区：
文献类型：
--
作者：
Flexer A;Stevens J
本文关注的是hubness的影响，在高维空间中的机器学习的一般问题，在现实世界中的音乐推荐系统的基础上可视化的k-最近邻（knn）图。由于在高维度中测量距离的问题，中枢对象被反复推荐，而反中枢对象在推荐列表中不存在，导致音乐目录的可达性差。我们提出了相互接近图，这是一种替代knn和相互knn图，并能够避免枢纽顶点具有异常高的连通性。我们发现，相互邻近图产生更好的图的连通性，从而提高可达性相比，knn图，相互knn图和相互knn图增强最小生成树，同时减少了枢纽的负面影响。
This paper is concerned with the impact of hubness, a general problem of machine learning in high-dimensional spaces, on a real-world music recommendation system based on visualisation of a k-nearest neighbour (knn) graph. Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists, resulting in poor reachability of the music catalogue. We present mutual proximity graphs, which are an alternative to knn and mutual knn graphs, and are able to avoid hub vertices having abnormally high connectivity. We show that mutual proximity graphs yield much better graph connectivity resulting in improved reachability compared to knn graphs, mutual knn graphs and mutual knn graphs enhanced with minimum spanning trees, while simultaneously reducing the negative effects of hubness.
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影响因子：
6
作者：
Flexer, Arthur;Schnitzer, Dominik
通讯作者：
Schnitzer, Dominik
影响因子：
8
作者：
Aucouturier, Jean-Julien;Pachet, Francois
通讯作者：
Pachet, Francois
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
影响因子：
0.8
作者：
Eppstein, D;Paterson, MS;Yao, FF
通讯作者：
Yao, FF
DOI：
10.1109/tkde.2007.1037
发表时间：
2007-07-01
影响因子：
8.9
作者：
Francois, Damien;Wertz, Vincent;Verleysen, Michel
通讯作者：
Verleysen, Michel