Traversing semantically annotated queries for task-oriented query recommendation

As search systems gradually turn into intelligent personal assistants, users increasingly resort to a search engine to accomplish a complex task, such as planning a trip, renting an apartment, or investing in stocks. A key challenge for the search engine is to understand the user's underlying task given a sample query like "tickets to panama", "studios in los angeles", or "spotify stocks", and to suggest other queries to help the user complete the task. In this paper, we investigate several strategies for query recommendation by traversing a semantically annotated query log using a mixture of explicit and latent representations of entire queries and of query segments. Our results demonstrate the effectiveness of these strategies in terms of utility and diversity, as well as their complementarity, with significant improvements compared to state-of-the-art query recommendation baselines adapted for this task.