Sha Yang

TL;DR: This paper empirically model the relationship between different sponsored search metrics such as click-through rates, conversion rates, cost per click, and ranking of advertisements and proposes a novel framework to better understand the factors that drive differences in these metrics.

TL;DR: In this paper, the authors use a hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo methods to quantify the relationship between various keyword characteristics, position of the advertisement, and landing page quality score on consumer search and purchase behavior as well as on advertiser's cost per click and the search engine's ranking decision.

TL;DR: This paper model and estimate the interrelationship between organic search listings and paid search advertisements, and suggests that click-throughs on organic listings have a positive interdependence with click- throughs on paid listings, and vice versa.

TL;DR: In this article, a Bayesian spatial autoregressive discrete-choice model is introduced to study the preference interdependence among individual consumers, and the authors illustrate their model of interdependent preferences with data on automobile purchases and show that preferences for Japanese-made cars are related to geographically and demographically defined networks.

TL;DR: In this article, a hierarchical Bayesian modeling framework is used to estimate the relationship between organic search listings and paid search advertisements, and it is shown that click-throughs on organic listings have a positive interdependence with click-out on paid listings, and vice versa.