In Dynamic Information Retrieval modeling we model dynamic systems which change or adapt over time or a sequence of events using a range of techniques from artificial intelligence and reinforcement learning. Many of the open problems in current IR research can be described as dynamic systems, for instance, session search or computational advertising. State of the art research provides solutions to these problems that are responsive to a changing environment, learn from past interactions and predict future utility. Advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way. The objective of this half-day tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling. We motivate a conceptual model linking static, interactive and dynamic retrieval and use this to define dynamics within the context of IR. We then cover a number of algorithms and techniques from the artificial intelligence (AI) and online learning literature such as Markov Decision Processes (MDP), their partially observable variation (POMDP) and multi-armed bandits.

Queries issued to a search engine are often under-specified or ambiguous. The user's search context or background may provide information that disambiguates their information need in order to automatically predict and issue a more effective query. The disambiguation can take place at different stages of the retrieval process. For instance, contextual query suggestions may be computed and recommended to users on the result page when appropriate, an approach that does not require modifying the original query's results. Alternatively, the search engine can attempt to provide efficient access to new relevant documents by injecting these documents directly into search results based on the user's context.

Dynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and information filtering systems. Existing IR techniques are limited in their ability to optimize over changes, learn with minimal computational footprint and be responsive and adaptive. The objective of this tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. It will cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics.

Modern information retrieval interfaces typically involve multiple pages of search results, and users who are recall minded or engaging in exploratory search using ad hoc queries are likely to access more than one page. Document rankings for such queries can be improved by allowing additional context to the query to be provided by the user herself using explicit ratings or implicit actions such as clickthroughs. Existing methods using this information usually involved detrimental UI changes that can lower user satisfaction. Instead, we propose a new feedback scheme that makes use of existing UIs and does not alter user's browsing behaviour; to maximise retrieval performance over multiple result pages, we propose a novel retrieval optimisation framework and show that the optimal ranking policy should choose a diverse, exploratory ranking to display on the first page. Then, a personalised re-ranking of the next pages can be generated based on the user's feedback from the first page. We show that document correlations used in result diversification have a significant impact on relevance feedback and its effectiveness over a search session. TREC evaluations demonstrate that our optimal rank strategy (including approximative Monte Carlo Sampling) can naturally optimise the trade-off between exploration and exploitation and maximise the overall user's satisfaction over time against a number of similar baselines.