  Getting a better understanding of user behavior is important for advancing
information retrieval systems. Existing work focuses on modeling and predicting
single interaction events, such as clicks. In this paper, we for the first time
focus on modeling and predicting sequences of interaction events. And in
particular, sequences of clicks.
  We formulate the problem of click sequence prediction and propose a click
sequence model (CSM) that aims to predict the order in which a user will
interact with search engine results. CSM is based on a neural network that
follows the encoder-decoder architecture. The encoder computes contextual
embeddings of the results. The decoder predicts the sequence of positions of
the clicked results. It uses an attention mechanism to extract necessary
information about the results at each timestep. We optimize the parameters of
CSM by maximizing the likelihood of observed click sequences.
  We test the effectiveness of CSM on three new tasks: (i) predicting click
sequences, (ii) predicting the number of clicks, and (iii) predicting whether
or not a user will interact with the results in the order these results are
presented on a search engine result page (SERP). Also, we show that CSM
achieves state-of-the-art results on a standard click prediction task, where
the goal is to predict an unordered set of results a user will click on.
