#Content-based recommendations with Poisson factorization ##by Prem Gopalan, Laurent Charlin, and David Blei, NIPS 2014.
We develop collaborative topic Poisson factorization (CTPF), a generative model of articles and reader preferences. CTPF can be used to build recommender systems by learning from reader histories and content to recommend personalized articles of interest. In detail, CTPF models both reader behavior and article texts with Poisson distributions, connecting the latent topics that represent the texts with the latent preferences that represent the readers. This provides better recommendations than competing methods and gives an interpretable latent space for understanding patterns of readership. Further, we exploit stochastic variational inference to model massive real-world datasets. For example, we can fit CPTF to the full arXiv usage dataset, which contains over 43 million ratings and 42 million word counts, within a day. We demonstrate empirically that our model outperforms several baselines, including the previous state-of-the art approach.
The paper is available from:
And from the conference website:
The C/C++ for CTPF is available from: https://github.com/premgopalan/collabtm
Source of the paper is in tex/
All figures from the paper are available from fig/. The raw data used to generate figures that contain experimental results are in fig/dat/. They can be generated using the R-scripts in fig/src/.