| Summary: | 碩士 === 國立政治大學 === 統計學系 === 106 === In this paper, we present two types of online learning algorithms--statical and dynamical--to capture users’ and items’ latent traits’ information through online product rating data in a real-time manner. The statical one extends Weng and Coad (2018)’s deterministic moment-matching method by adding priors to cutpoints, and the dynamical one extends the statical one with the dynamical ideas adopted in Graepel et al. (2010) for taking users’ and items’ time-dependent latent traits into account. Both learning algorithms are designed for the Bayesian ordinal IRT model proposed by Ho and Quinn (2008).
Through experiments, we have verified two things: First, updating cutpoints sequentially produces better results. Second, statical learning’s computational time is almost twice as less as dynamical learning’s, but dynamical learning can
slightly outperform statical learning under some configurations.
At the end of the paper, we give some useful configurations for setting up the priors of the latent variables of Ho and Quinn’s ordinal IRT model.
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