Deep Belief Nets for Topic Modeling
This addresses the challenge of sparse user data in digital publishing by leveraging textual content for recommendations, but it is incremental as it compares existing methods on new data.
The paper tackled the problem of content-based collaborative filtering for digital publishing by comparing latent Dirichlet allocation (LDA) and deep belief nets (DBN) to find low-dimensional latent representations for documents, working on public benchmarks and digital media content from Issuu.
Applying traditional collaborative filtering to digital publishing is challenging because user data is very sparse due to the high volume of documents relative to the number of users. Content based approaches, on the other hand, is attractive because textual content is often very informative. In this paper we describe large-scale content based collaborative filtering for digital publishing. To solve the digital publishing recommender problem we compare two approaches: latent Dirichlet allocation (LDA) and deep belief nets (DBN) that both find low-dimensional latent representations for documents. Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an online publishing platform. This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance evaluation of the DBN model and comparisons to the LDA model.