Deep Belief Nets for Topic Modeling

  • Maaløe, Lars (Project Participant)
  • Winther, Ole (Main Supervisor)

Project Details

Description

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 project also comes with a deep belief nets toolbox for topic modeling tailored towards performance evaluation of the DBN model and comparisons to the LDA model.
StatusFinished
Effective start/end date01/02/201301/03/2014

Keywords

  • Deep learning
  • Topic modeling
  • Machine learning
  • Neural network

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  • Deep Belief Nets for Topic Modeling

    Maaløe, L., Arngren, M. & Winther, O., 2015, Proceedings of the 31st International Conference on Machine Learning (ICML 2014): JMLR Workshop and Conference Proceedings. Xing, E. P. & Jebara, T. (eds.). Vol. 32. 5 p.

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