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Abstract:
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In this talk, we consider the problem of using a database of book reviews to inform user-targeted recommendations. In our dataset, books are categorized into genres and sub-genres. We use a hierarchical model, which is able to exploit this nested structure to pool information across similar items at many levels within the genre hierarchy simultaneously. Our main challenge is that fitting our model at scale using off-the-shelf maximum likelihood procedures is prohibitive due to the large data sizes involved. To get around this, we extend a moment-based fitting procedure proposed by Perry in 2016 for fitting two-level hierarchical models, which is an order of magnetite faster than maximum likelihood. Our procedure can be used in other contexts for fitting deeply-nested hierarchical generalized linear mixed models efficiently.
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