Why Did You Cover That Song?: Modeling N-th Order Derivative Creation with Content Popularity

Why Did You Cover That Song?: Modeling N-th Order Derivative Creation with Content Popularity
复制标题

你为什么翻唱那首歌?:利用内容流行度对 N 阶衍生作品进行建模

DOI:
10.1145/2983323.2983674
复制
发表时间:
2016
期刊:
Proceedings of 25th ACM International Conference on Information and Knowledge Management (CIKM 2016)
影响因子:
--
通讯作者:
Masataka Goto
Masataka Goto
中科院分区:
--
文献类型:
--
作者:
Kosetsu Tsukuda;Masahiro Hamasaki;Masataka Goto

文献摘要

参考文献

被引文献

相似文献

许多业余创作者现在创作衍生作品并将其放在网上。虽然有几个因素激发了衍生作品的创作,但这些因素通常无法在网络上观察到。在本文中,我们提出了一个模型来推断潜在的因素从衍生工作发布事件的序列。我们假设一个序列是一个随机过程,包含以下三个因素:(1)原始作品的吸引力,(2)原始作品的受欢迎程度,(3)衍生作品的受欢迎程度。为了表征内容受欢迎程度,我们使用内容排名数据,并根据创作者的浏览行为纳入排名偏向的受欢迎程度。我们的主要贡献有三个方面:(1)据我们所知,这是第一次对衍生创作活动进行建模的研究,(2)通过使用与音乐相关的衍生作品创作的真实数据集来评估我们的模型,我们展示了采用所有三个因素来建模衍生创作活动并考虑创作者浏览行为的有效性,(3)定性实验表明,该模型可以从类别特征、衍生作品发布事件触发因素的时间发展等方面分析衍生作品创作活动。
Many amateur creators now create derivative works and put them on the web. Although there are several factors that inspire the creation of derivative works, such factors cannot usually be observed on the web. In this paper, we propose a model for inferring latent factors from sequences of derivative work posting events. We assume a sequence to be a stochastic process incorporating the following three factors: (1) the original work's attractiveness, (2) the original work's popularity, and (3) the derivative work's popularity. To characterize content popularity, we use content ranking data and incorporate rank-biased popularity based on the creators' browsing behavior. Our main contributions are three-fold: (1) to the best of our knowledge, this is the first study modeling derivative creation activity, (2) by using a real-world dataset of music-related derivative work creation to evaluate our model, we showed the effectiveness of adopting all three factors to model derivative creation activity and onsidering creators' browsing behavior, and (3) we carried out qualitative experiments and showed that our model is useful in analyzing derivative creation activity in terms of category characteristics, temporal development of factors that trigger derivative work posting events, etc.
音乐信息研究的巨大挑战
DOI: --
发表时间: 2012
期刊: Multimodal Music Processing
影响因子: --
作者:
Masataka Goto
通讯作者: Masataka Goto
DOI: 10.1145/1556460.1556485
发表时间: 2009
期刊: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
影响因子: --
作者:
G. Cheliotis;Jude Yew
通讯作者: Jude Yew
Modulobe:复杂运动铰接模型的创建和共享平台
DOI: 10.1145/1501750.1501823
发表时间: 2008
期刊: --
影响因子: --
作者:
Kouichirou Eto;Masahiro Hamasaki;Kuniaki Watanabe;Yoshinori Kawasaki;Takuichi Nishimura
通讯作者: Takuichi Nishimura
多媒体内容大规模协作创作的网络分析:nico nico douga 上的初音未来视频案例研究
DOI: 10.1145/1453805.1453838
发表时间: 2008
期刊: Proceedings of the 9th International Symposium on Open Collaboration
影响因子: --
作者:
Masahiro Hamasaki;Hideaki Takeda;Takuichi Nishimura
通讯作者: Takuichi Nishimura