FITTING A DEEPLY NESTED HIERARCHICAL MODEL TO A LARGE BOOK REVIEW DATASET USING A MOMENT-BASED ESTIMATOR
成果类型：
Review
署名作者：
Zhang, Ningshan; Schmaus, Kyle; Perry, Patrick O.
署名单位：
New York University
刊物名称：
ANNALS OF APPLIED STATISTICS
ISSN/ISSBN：
1932-6157
DOI：
10.1214/19-AOAS1251
发表日期：
2019
页码：
2260-2288
关键词：
摘要：
We consider a particular instance of a common problem in recommender systems, using a database of book reviews to inform user-targeted recommendations. In our dataset, books are categorized into genres and subgenres. To exploit this nested taxonomy, we use a hierarchical model that enables information pooling across across similar items at many levels within the genre hierarchy. The main challenge in deploying this model is computational. The data sizes are large and fitting the model at scale using off-the-shelf maximum likelihood procedures is prohibitive. To get around this computational bottleneck, we extend a moment-based fitting procedure proposed for fitting single-level hierarchical models to the general case of arbitrarily deep hierarchies. This extension is an order of magnitude faster than standard maximum like-lihood procedures. The fitting method can be deployed beyond recommender systems to general contexts with deeply nested hierarchical generalized linear mixed models.
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