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 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...

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...

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...