A full paper on A Context-aware Time Model for Web Search by Alexey Borisov, Ilya Markov, Maarten de Rijke, and Pavel Serdyukov will be presented at the ACM International Conference on Research and Development in Information Retrieval (SIGIR 2016) in Pisa, Italy. The paper models the time between user actions in web search. It reveals that such times are affected by the context in which they are observed. The paper uses neural networks to automatically detect and remove various source of context bias.

Abstract:
In web search, information about times between user actions has
been shown to be a good indicator of users’ satisfaction with the
search results. Existing work uses the mean values of the observed
times, or fits probability distributions to the observed times. This
implies a context-independence assumption that the time elapsed
between a pair of user actions does not depend on the context, in
which the first action takes place. We validate this assumption using logs of a commercial web search engine and discover that it
does not always hold. For between 37% to 80% of query-result
pairs, depending on the number of observations, the distributions of
click dwell times have statistically significant differences in query
sessions for which a given result (i) is the first item to be clicked
and (ii) is not the first. To account for this context bias effect,
we propose a context-aware time model (CATM). The CATM allows us (i) to predict times between user actions in contexts, in
which these actions were not observed, and (ii) to compute context-independent estimates of the times by predicting them in predefined
contexts. Our experimental results show that the CATM provides
better means than existing methods to predict and interpret times
between user actions.