  Click logs are valuable resources for a variety of information retrieval (IR)
tasks. This includes query understanding/analysis, as well as learning
effective IR models particularly when the models require large amounts of
training data. We release a large-scale domain-specific dataset of click logs,
obtained from user interactions of the Trip Database health web search engine.
Our click log dataset comprises approximately 5.2 million user interactions
collected between 2013 and 2020. We use this dataset to create a standard IR
evaluation benchmark -- TripClick -- with around 700,000 unique free-text
queries and 1.3 million pairs of query-document relevance signals, whose
relevance is estimated by two click-through models. As such, the collection is
one of the few datasets offering the necessary data richness and scale to train
neural IR models with a large amount of parameters, and notably the first in
the health domain. Using TripClick, we conduct experiments to evaluate a
variety of IR models, showing the benefits of exploiting this data to train
neural architectures. In particular, the evaluation results show that the best
performing neural IR model significantly improves the performance by a large
margin relative to classical IR models, especially for more frequent queries.
