Submitting queries to search engines has become a major way in which consumers search for information and products. The massive amount of search query data available today has the potential to provide valuable information on consumer preferences. In order to unlock this potential, it is...

We study and model the process by which humans summarize creative documents (e.g., from a movie script to a synopsis). We develop a customized topic model based on Poisson Factorization and inspired by the creativity literature, which links the text in a summary to the text in the original...

We explore how consumers form online search queries, given their content preferences. In particular, we explore the role of activation probabilities from queries to results, i.e., the probability that a certain query will retrieve certain content. Using secondary data from Google, we first show...

Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there...

We extend Latent Dirichlet Allocation (LDA) by introducing a topic model, Hierarchically Dual Latent Dirichlet Allocation (HDLDA), for contexts in which one type of documents (e.g., search queries) are semantically related to another type of documents (e.g., search results). In the context of...

Logos serve a fundamental role as the visual figureheads of brands. Yet, due to the difficulty of using unstructured image data, prior research on logo design has largely been limited to non-quantitative studies. In this work, we explore the interplay between logo design and brand identity...