  Previous work has shown that popular trending events are important external
factors which pose significant influence on user search behavior and also
provided a way to computationally model this influence. However, their problem
formulation was based on the strong assumption that each event poses its
influence independently. This assumption is unrealistic as there are many
correlated events in the real world which influence each other and thus, would
pose a joint influence on the user search behavior rather than posing influence
independently. In this paper, we study this novel problem of Modeling the Joint
Influences posed by multiple correlated events on user search behavior. We
propose a Joint Influence Model based on the Multivariate Hawkes Process which
captures the inter-dependency among multiple events in terms of their influence
upon user search behavior. We evaluate the proposed Joint Influence Model using
two months query-log data from https://search.yahoo.com/. Experimental results
show that the model can indeed capture the temporal dynamics of the joint
influence over time and also achieves superior performance over different
baseline methods when applied to solve various interesting prediction problems
as well as real-word application scenarios, e.g., query auto-completion.
