When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help...

Search has arguably become the dominant paradigm for finding information on the World Wide Web. In order to build a successful search engine, there are a number of challenges that arise where techniques from artificial intelligence can be used to have a significant impact. In this paper, we...

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...

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...