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Content-Based Model of Web Search Behavior: An Application to TV Show Search

Author

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  • Jia Liu

    (Department of Marketing, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong)

  • Olivier Toubia

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Shawndra Hill

    (Wharton Customer Analytics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

We develop a flexible content-based search model that links the content preferences of search engine users to query search volume and click-through rates, while allowing content preferences to vary systematically based on the context of a search. Content preferences are defined over latent topics that describe the content of search queries and search result descriptions. Compared with existing applications of topic modeling in marketing and recommendation systems, our proposed approach can simultaneously capture multiple types of information and investigate multiple aspects of behavioral dynamics in a single framework that enables interpretable results for business decision making. To facilitate efficient and scalable inference, we develop a full Bayesian variational inference algorithm. We evaluate our modeling framework using real-world search data for TV shows from the Bing search engine. We illustrate how our model can quantify the content preferences associated with each query and how these preferences vary systematically based on whether the query is observed before, during, or after a TV show is aired. We also show that our model can help the search engine improve its ranking of search results as well as address the cold-start problem for new page links.

Suggested Citation

  • Jia Liu & Olivier Toubia & Shawndra Hill, 2021. "Content-Based Model of Web Search Behavior: An Application to TV Show Search," Management Science, INFORMS, vol. 67(10), pages 6378-6398, October.
  • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:10:p:6378-6398
    DOI: 10.1287/mnsc.2020.3827
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    References listed on IDEAS

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    1. Sridhar Narayanan & Kirthi Kalyanam, 2015. "Position Effects in Search Advertising and their Moderators: A Regression Discontinuity Approach," Marketing Science, INFORMS, vol. 34(3), pages 388-407, May.
    2. Babur De los Santos & Sergei Koulayev, 2017. "Optimizing Click-Through in Online Rankings with Endogenous Search Refinement," Marketing Science, INFORMS, vol. 36(4), pages 542-564, July.
    3. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    4. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    5. Jia Liu & Shawndra Hill, 2021. "Frontiers: Moment Marketing: Measuring Dynamics in Cross-Channel Ad Effectiveness," Marketing Science, INFORMS, vol. 40(1), pages 13-22, January.
    6. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    7. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.
    8. Jia Liu & Olivier Toubia, 2020. "Search query formation by strategic consumers," Quantitative Marketing and Economics (QME), Springer, vol. 18(2), pages 155-194, June.
    9. Sha Yang & Anindya Ghose, 2010. "Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?," Marketing Science, INFORMS, vol. 29(4), pages 602-623, 07-08.
    10. Beth L. Fossen & David A. Schweidel, 2017. "Television Advertising and Online Word-of-Mouth: An Empirical Investigation of Social TV Activity," Marketing Science, INFORMS, vol. 36(1), pages 105-123, January.
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