  Many e-commerce websites use recommender systems or personalized rankers to
personalize search results based on their previous interactions. However, a
large fraction of users has no prior inter-actions, making it impossible to use
collaborative filtering or rely on user history for personalization. Even the
most active users mayvisit only a few times a year and may have volatile needs
or different personas, making their personal history a sparse and noisy signal
at best. This paper investigates how, when we cannot rely on the user history,
the large scale availability of other user interactions still allows us to
build meaningful profiles from the contextual data and whether such contextual
profiles are useful to customize the ranking, exemplified by data from a major
online travel agentBooking.com.Our main findings are threefold: First, we
characterize the Continuous Cold Start Problem(CoCoS) from the viewpoint of
typical e-commerce applications. Second, as explicit situational con-text is
not available in typical real world applications, implicit cues from
transaction logs used at scale can capture essential features of situational
context. Third, contextual user profiles can be created offline, resulting in a
set of smaller models compared to a single huge non-contextual model, making
contextual ranking available with negligible CPU and memory footprint. Finally
we conclude that, in an online A/B test on live users, our contextual ranker
in-creased user engagement substantially over a non-contextual base-line, with
click-through-rate (CTR) increased by 20%. This clearly demonstrates the value
of contextual user profiles in a real world application.
