  A frequent pattern in customer care conversations is the agents responding
with appropriate webpage URLs that address users' needs. We study the task of
predicting the documents that customer care agents can use to facilitate users'
needs. We also introduce a new public dataset which supports the aforementioned
problem. Using this dataset and two others, we investigate state-of-the art
deep learning (DL) and information retrieval (IR) models for the task.
Additionally, we analyze the practicality of such systems in terms of inference
time complexity. Our show that an hybrid IR+DL approach provides the best of
both worlds.
