We introduce the novel task of answering entity-seeking recommendation questions using a collection of reviews that describe candidate answer entities. We harvest a QA dataset that contains 47,124 paragraph-sized real user questions from travelers seeking recommendations for hotels, attractions and restaurants. Each question can have thousands of candidate answers to choose from and each candidate is associated with a collection of unstructured reviews. This dataset is especially challenging because commonly used neural architectures for reasoning and QA are prohibitively expensive for a task of this scale. As a solution, we design a scalable cluster-select-rerank approach. It first clusters text for each entity to identify exemplar sentences describing an entity. It then uses a scalable neural information retrieval (IR) module to select a set of potential entities from the large candidate set. A reranker uses a deeper attention-based architecture to pick the best answers from the selected entities. This strategy performs better than a pure IR or a pure attention-based reasoning approach yielding nearly 25% relative improvement in Accuracy@3 over both approaches.
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