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

Jia Liu () and Olivier Toubia ()
Additional contact information
Jia Liu: Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Olivier Toubia: Columbia Business School, Columbia University, New York, New York 10025

Marketing Science, 2018, vol. 37, issue 6, 930-952

Abstract: We extend latent Dirichlet allocation by introducing a topic model, hierarchically dual latent Dirichlet allocation (HDLDA), for contexts in which one type of document (e.g., search queries) are semantically related to another type of document (e.g., search results). In the context of online search engines, HDLDA identifies not only topics in short search queries and web pages, 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 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
https://doi.org/10.1287/mksc.2018.1112 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:37:y:2018:i:6:p:930-952

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().