  For ambiguous queries, conventional retrieval systems are bound by two
conflicting goals. On the one hand, they should diversify and strive to present
results for as many query intents as possible. On the other hand, they should
provide depth for each intent by displaying more than a single result. Since
both diversity and depth cannot be achieved simultaneously in the conventional
static retrieval model, we propose a new dynamic ranking approach. Dynamic
ranking models allow users to adapt the ranking through interaction, thus
overcoming the constraints of presenting a one-size-fits-all static ranking. In
particular, we propose a new two-level dynamic ranking model for presenting
search results to the user. In this model, a user's interactions with the
first-level ranking are used to infer this user's intent, so that second-level
rankings can be inserted to provide more results relevant for this intent.
Unlike for previous dynamic ranking models, we provide an algorithm to
efficiently compute dynamic rankings with provable approximation guarantees for
a large family of performance measures. We also propose the first principled
algorithm for learning dynamic ranking functions from training data. In
addition to the theoretical results, we provide empirical evidence
demonstrating the gains in retrieval quality that our method achieves over
conventional approaches.
