Probabilistic latent preference analysis for collaborative filtering

  title={Probabilistic latent preference analysis for collaborative filtering},
  author={Nathan Nan Liu and Min Zhao and Qiang Yang},
A central goal of collaborative filtering (CF) is to rank items by their utilities with respect to individual users in order to make personalized recommendations. Traditionally, this is often formulated as a rating prediction problem. However, it is more desirable for CF algorithms to address the ranking problem directly without going through an extra rating prediction step. In this paper, we propose the probabilistic latent preference analysis (pLPA) model for ranking predictions by directly… CONTINUE READING
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