Relational Stacked Denoising Autoencoder for Tag Recommendation
Relational Stacked Denoising Autoencoder for Tag Recommendation
复制标题
DOI：
10.1609/aaai.v31i1.9548
复制
发表时间：
2015-01
期刊：
影响因子：
--
通讯作者：
Hao Wang;Xingjian Shi;D. Yeung
中科院分区：
文献类型：
--
作者：
Hao Wang;Xingjian Shi;D. Yeung
标签推荐已成为对文章、电影和音乐等在线资源进行组织和索引的最重要方式之一。由于标签信息通常非常稀疏，对这些资源的内容表示进行有效学习对于准确的标签推荐至关重要。最近，为标签推荐提出的模型，如协同主题回归及其变体，已显示出有前景的准确性。然而，这些模型的一个局限是，通过使用像潜在狄利克雷分配这样的主题模型作为关键组件，所学习到的表示可能不够紧凑和有效。此外，由于关系数据在许多应用中作为辅助数据源存在，将此类数据纳入标签推荐模型是可取的。在本文中，我们从一种称为堆叠去噪自编码器（SDAE）的深度学习模型入手，试图学习更有效的内容表示。我们为SDAE提出了一种概率公式，然后将其扩展为一个关系型SDAE（RSDAE）模型。RSDAE在一个概率框架下以一种有原则的方式联合进行深度表示学习和关系学习。在三个真实数据集上进行的实验表明，学习更有效的表示以及从关系数据中学习都是推进现有技术水平的有益步骤。
Tag recommendation has become one of the most important ways of organizing and indexing online resources like articles, movies, and music. Since tagging information is usually very sparse, effective learning of the content representation for these resources is crucial to accurate tag recommendation. Recently, models proposed for tag recommendation, such as collaborative topic regression and its variants, have demonstrated promising accuracy. However, a limitation of these models is that, by using topic models like latent Dirichlet allocation as the key component, the learned representation may not be compact and effective enough. Moreover, since relational data exist as an auxiliary data source in many applications, it is desirable to incorporate such data into tag recommendation models. In this paper, we start with a deep learning model called stacked denoising autoencoder (SDAE) in an attempt to learn more effective content representation. We propose a probabilistic formulation for SDAE and then extend it to a relational SDAE (RSDAE) model. RSDAE jointly performs deep representation learning and relational learning in a principled way under a probabilistic framework. Experiments conducted on three real datasets show that both learning more effective representation and learning from relational data are beneficial steps to take to advance the state of the art.