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Neural networks with deep architectures have demonstrated significantperformance improvements in computer vision, speech recognition, and naturallanguage processing. The challenges in information retrieval (IR), however, aredifferent from these other application areas. A common form of IR involvesranking of documents--or short passages--in response to keyword-based queries.Effective IR systems must deal with query-document vocabulary mismatch problem,by modeling relationships between different query and document terms and howthey indicate relevance. Models should also consider lexical matches when thequery contains rare terms--such as a person's name or a product modelnumber--not seen during training, and to avoid retrieving semantically relatedbut irrelevant results. In many real-life IR tasks, the retrieval involvesextremely large collections--such as the document index of a commercial Websearch engine--containing billions of documents. Efficient IR methods shouldtake advantage of specialized IR data structures, such as inverted index, toefficiently retrieve from large collections. Given an information need, the IRsystem also mediates how much exposure an information artifact receives bydeciding whether it should be displayed, and where it should be positioned,among other results. Exposure-aware IR systems may optimize for additionalobjectives, besides relevance, such as parity of exposure for retrieved itemsand content publishers. In this thesis, we present novel neural architecturesand methods motivated by the specific needs and challenges of IR tasks.

Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval