  Most successful search queries do not result in a click if the user can
satisfy their information needs directly on the SERP. Modeling query
abandonment in the absence of click-through data is challenging because search
engines must rely on other behavioral signals to understand the underlying
search intent. We show that mouse cursor movements make a valuable, low-cost
behavioral signal that can discriminate good and bad abandonment. We model
mouse movements on SERPs using recurrent neural nets and explore several data
representations that do not rely on expensive hand-crafted features and do not
depend on a particular SERP structure. We also experiment with data resampling
and augmentation techniques that we adopt for sequential data. Our results can
help search providers to gauge user satisfaction for queries without clicks and
ultimately contribute to a better understanding of search engine performance.
