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Abstract:
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A common problem in recommendation systems is the cold start problem: how can we make a recommendation to a new customer, without any prior purchase data? Such problems are particularly salient for increasingly common online subscription businesses, where initial recommendations can shape whether potential customers decide to subscribe, and how their preferences evolve subsequently. The need to assess a new customers’ preferences quickly, and without prior purchase data, has led to the increasing prevalence of customer on-boarding surveys, wherein companies ask potential or current customers a series of questions aimed at understanding their preferences, without having observed any purchasing. In this work, we bridge the on-boarding problem and classic methods for adaptive preference measurement, using a combination of representation learning for unstructured data, and Bayesian optimization for on-the-fly estimation of preferences. We apply this framework both in the context of an on-boarding survey for an online subscription business, and in the context of traditional preference measurement.
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