Looking for the Movie Seven or Sven from the Movie Frozen?: A Multi-perspective Strategy for Recommending Queries for Children

Abstract

Popular search engines are usually tuned to satisfy the information needs of a general audience. As a result, non-traditional, yet active groups of users, such as children, experience challenges composing queries that can lead them to the retrieval of adequate results. To aid young users in formulating keyword queries that can facilitate their information-seeking process, we introduce ReQuIK, a multi-perspective query suggestion system for 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. For assessing the quality of the system, we conducted initial offline and online experiments based on 591 queries written by 97 children, ages 6 to 13. The results of this assessment verified the correctness of ReQuIK»s recommendation strategy, the fact that it provides suggestions that appeal to children and ReQuIK»s ability to recommend queries that lead to the retrieval of materials with readability levels that correlate with children»s reading skills.