Strathprints Institutional Repository Bayesian Latent Variable Models for Collaborative Item Rating Prediction. In: Acm Cikm, 2011-03-31. Bayesian Latent Variable Models for Collaborative Item…

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Nonparametric bayesian multitask collaborative filtering (2013, methodology): “The settings of the BLITR model are adopted from [9]: a Rao-Blackwellised Gibbs sampler is used to draw 300 samples from the Markov chain, with a burn-in of 200 samples.”
Personalized Recommendation Combining User Interest and Social Circle (2014, unknown): “It can be viewed as the first generation of RSes [7] with traditional collaborative filtering algorithms [8]– [12], [22]–[32] to predict user interest.”
A Coupled Indian Buffet Process Model for Collaborative Filtering (2012, methodology): “…and significance weighting (nearest-neighbor) (Breese et al., 1998), a regularized gradient descent SVD method with user and item biases (Paterek, 2007), and the model-based BLITR method (Harvey et al., 2011), which postulates a statistical model inspired by principles employed in LDA.”
Social contextual recommendation (2012, methodology): “The model-based methods learn a model based on patterns recognized in the ratings of users using Bayesian networks and other clustering techniques [6, 8, 19].”
Ieee Transactions on Knowledge and Data Engineering, Manuscropt Id 1 Social Recommendation with Cross-domain Transferable Knowledge (unknown): “Collaborative filtering (CF) techniques are a common approach to recommendation, and have been applied in real recommender systems [11][12][13].”