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A Bayesian Framework for Modeling Price Preference in Product Search

Product search is an emerging search application where optimization of search results relies critically on an accurate model of a user’s price preference. In this paper, we propose a Bayesian framework for modeling a user’s price preference with a particular focus on developing a smart price filter model for inferring a user’s price preference based on the user’s selection of price filters and optimizing ranking of products accordingly. Preliminary experiment results with product search log show promise of the framework, which opens up interesting opportunities for new research in the intersection of machine learning, information retrieval and economics.