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

By Jia Liu, Olivier Toubia. 2018

79 citations recorded by the source corpus.

We extend latent Dirichlet allocation by introducing a topic model, hierarchically dual latent Dirichlet allocation, the output of which provides a basis for estimating consumers’ content preferenc...

Subfields: Artificial Intelligence, Marketing, Signal Processing

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Citations in the corpus

Manipulating and Measuring Model Interpretability (2018, background): “ent [33], judicial sentencing and bail [3, 14, 35, 37], and hiring [43]. In addition, these models inﬂuence decisions about what news people see on social media platforms (e.g., Twitter and Facebook, [2, 10, 48, 61]), provide recommendations for what movies to watch (e.g., Netﬂix, [7]) or what music”
Machine learning and AI in marketing – Connecting computing power to human insights (2020, unknown): “Automated agents are likely to reach every aspect of marketing, from marketing mix decisions, to customer engagement and relationship management (Ma et al., 2015; Schweidel & Moe, 2014), to cross-channel coordination and attribution (de Haan, Kannan, & Verhoef, 2018; Li & Kannan, 2014), to search…”
Understanding Large-Scale Dynamic Purchase Behavior (2020, unknown): “Most of these papers involve analyzing textual data (Tirunillai and Tellis 2014, Büschken and Allenby 2016, Rutz et al. 2017, Puranam et al. 2017, Liu and Toubia 2018, Büschken and Allenby 2020), which is the traditional application of a topic model.”