Query recommendation in the information domain of children

Children represent an increasing group of web users. Some of the key problems that hamper their search experience is their limited vocabulary, their difficulty in using the right keywords, and the inappropriateness of their general‐purpose query suggestions. In this work, we propose a method that uses tags from social media to suggest queries related to children's topics. Concretely, we propose a simple yet effective approach to bias a random walk defined on a bipartite graph of web resources and tags through keywords that are more commonly used to describe resources for children. We evaluate our method using a large query log sample of queries submitted by children. We show that our method outperforms by a large margin the query suggestions of modern search engines and state‐of‐the art query suggestions based on random walks. We improve further the quality of the ranking by combining the score of the random walk with topical and language modeling features to emphasize even more the child‐related aspects of the query suggestions.

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