Relational Stacked Denoising Autoencoder for Tag Recommendation

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 ...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Hao Wang, Xingjian Shi, Dit-Yan Yeung
• Keywords: AAAI 2015, Intelligent Agents