A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries

59 Pages Posted: 19 Dec 2015 Last revised: 17 Apr 2019

See all articles by Jia Liu

Jia Liu

Hong Kong University of Science & Technology (HKUST) - HKUST Business School

Olivier Toubia

Columbia Business School - Marketing

Date Written: August 7, 2018

Abstract

We extend Latent Dirichlet Allocation (LDA) by introducing a topic model, Hierarchically Dual Latent Dirichlet Allocation (HDLDA), for contexts in which one type of documents (e.g., search queries) are semantically related to another type of documents (e.g., search results). In the context of online search engines, HDLDA identifies not only topics in short search queries and webpages, but also how the topics in search queries relate to the topics in the corresponding top search results. The output of HDLDA provides a basis for estimating consumers’ content preferences on the fly from their search queries, given a set of assumptions on how consumers translate their content preferences into search queries. We apply HDLDA and explore its use in the estimation of content preferences, in two studies. The first is a lab experiment in which we manipulate participants’ content preferences, and observe the queries they formulate and their browsing behavior, across different product categories. The second is a field study, which allows us to explore whether the content preferences estimated based on HDLDA may be used to explain and predict click-through rates in online search advertising.

Keywords: search engine optimization, search engine marketing, search queries, content preferences, semantic relationships, topic modeling

JEL Classification: M31

Suggested Citation

Liu, Jia and Toubia, Olivier, A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries (August 7, 2018). Columbia Business School Research Paper No. 16-2. Available at SSRN: https://ssrn.com/abstract=2705069 or http://dx.doi.org/10.2139/ssrn.2705069

Jia Liu (Contact Author)

Hong Kong University of Science & Technology (HKUST) - HKUST Business School ( email )

Clear Water Bay
Hong Kong

Olivier Toubia

Columbia Business School - Marketing ( email )

New York, NY 10027
United States

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