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As one of the largest communities that search for online resources, children are introduced to the Web at increasingly young ages. However, popular search tools are not explicitly designed with children in mind nor do their retrieved results explicitly target children. Consequently, many young users struggle in completing successful searches, especially since most search engines (SE) do not directly support, or offer weak support, for children’s inquiry approaches. Even though children, as inexperienced users, struggle with describing their information needs in a concise query, they still expect SEs to retrieve relevant information in response to their requirements. As part of their capabilities, SEs often suggest queries to aid users in better defining their information needs. In fact, a recent study conducted by Gossen et al. shows that children pay more attention to suggested queries than adults. Unfortunately, these suggestions are not specifically tailored towards children and thus need improvement. While there exist multiple query suggestion modules, only few specifically target children. To address this problem, along with a need for more children-related tools, we rely on ReQuIK (Recommendations based on Query Intention for Kids), a query suggestion module tailored towards 6-to-13 year old children. ReQuIK informs its suggestion process by applying (i) a strategy based on search intent to capture the purpose of a query, (ii) a ranking strategy based on a wide and deep neural network that considers both raw text and traits commonly associated with kid-related queries, (iii) a filtering strategy based on the readability levels of documents potentially retrieved by a query to favor suggestions that trigger the retrieval of documents matching children’s reading skills, and (iv) a content-similarity strategy to ensure diversity among suggestions.