Improving Search Engines via Large-Scale Physiological Sensing

Result ranking in commercial web search engines is based on a wide array of signals, from keywords appearing on web pages to behavioral (clickthrough) data aggregated across many users or from the current user only. The recent emergence of wearable devices has enabled the collection of physiological data such as heart rate, skin temperature, and galvanic skin response at a population scale. These data are useful for many public health tasks, but they may also provide novel clues about people’s interests and intentions as they engage in online activities. In this paper, we focus on heart rate and show that there are strong relationships between heart rate and various measures of user interest in a search result. We integrate features of heart rate, including heart rate dynamics, as additional attributes in a competitive machine-learned web search ranking algorithm. We show that we can obtain significant relevance improvements from this physiological sensing that vary depending on the search topic.