With music becoming an essential part of daily life, there is an urgent need to develop recommendation systems to assist people targeting better songs with fewer efforts. As the interactions between users and songs naturally construct a complex network, community detection approaches can be applied to reveal users' potential interests on songs by grouping relevant users & songs to the same community. However, as the types of interaction could be heterogeneous, it challenges conventional community detection methods designed originally for homogeneous networks. Although there are existing works on heterogeneous community detection, they are mostly task-driven approaches and not feasible for specific music recommendation. In this paper, we propose a genetic based approach to learn an edge-type usefulness distribution (ETUD) for all edge-types in heterogeneous music networks. ETUD can be regarded as a linear function to project all edges to the same latent space and make them comparable. Therefore a heterogeneous network can be converted to a homogeneous one where those conventional methods are eligible to use. We validate the proposed model on a heterogeneous music network constructed from an online music streaming service. Results show that for conventional methods, ETUD can help to detect communities significantly improving music recommendation accuracy while simultaneously reducing user searching cost.
翻译:随着音乐成为日常生活的一个基本部分,迫切需要发展建议系统,帮助人们以较少的努力选择更好的歌曲。随着用户和歌曲之间的互动自然地构建了一个复杂的网络,社区检测方法可以用来通过将相关用户和歌曲分组到同一个社区来揭示用户对歌曲的潜在兴趣。然而,由于互动的类型可能多种多样,它挑战了最初为同质网络设计的常规社区检测方法。虽然有关于异质社区检测的现有工作,但它们大多是任务驱动的方法,对于具体的音乐建议来说不可行。在本文中,我们建议一种基于基因的方法,为不同音乐网络中的所有边缘类型学习一种边缘类型的有用性分布(ETUD)。可以将ETUD视为一种线性功能,将所有边缘投射到相同的潜在空间,使其具有可比性。因此,在一个允许使用这些传统方法的地方,可以将混合网络转换为同质的网络。我们验证了从在线音乐流服务中构建的多元性音乐网络的拟议模式。结果显示,对于常规方法,ETUD可以帮助检测社区显著提高音乐建议的准确性,同时降低用户搜索成本。