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

66 Pages Posted: 7 May 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

Shawndra Hill

Microsoft Corporation - Microsoft Research, New York City

Date Written: March 28, 2019

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. Content preferences are defined over latent topics that describe the content of search queries and search result descriptions. Moreover, our model allows content preferences to vary systematically based on the context of the search. To facilitate efficient and scalable inference, we develop a full Bayesian variational inference algorithm. We evaluate our modelling framework using real-world data on searches for TV shows on 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. In addition to search engines, advertisers can apply our model to understand their search advertising campaign data and gain insights into which search ads should be targeted to which users, when, and on which devices.

Keywords: marketing, search, interpretable machine learning, recommendation systems, Poisson factorization, variational inference, big data

JEL Classification: M31

Suggested Citation

Liu, Jia and Toubia, Olivier and Hill, Shawndra, Content-Based Model of Web Search Behavior: An Application to TV Show Search (March 28, 2019). Available at SSRN: https://ssrn.com/abstract=3372751 or http://dx.doi.org/10.2139/ssrn.3372751

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

Shawndra Hill

Microsoft Corporation - Microsoft Research, New York City ( email )

641 Avenue of Americas
New York, NY 10011
United States

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