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The Internet offers tremendous opportunities for information sharing and content distribution. However, without proper filtering and selection, the large amount of information may likely swarm the users rather than benefit them. Collaborative filtering is a technique for extracting useful information from the large information pool generated by interconnected online communities. In this paper, the authors develop a probabilistic collaborative filtering algorithm, which is based on ordered logistic regression and takes into account both similarities among the users and similarities among the items. They make inference with maximum likelihood and Bayesian frameworks, and propose a Markov Chain Monte Carlo based Expectation Maximization algorithm to optimize model parameters.
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