Analyzing Book-Related Features to Recommend Books for Emergent Readers

ABSTRACT

We recognize that emergent literacy forms a foundation upon which children will gage their future reading. It is imperative to motivate young readers to read by offering them appealing books to read so that they can enjoy reading and gradually establish a reading habit during their formative years that can aid in promoting their good reading habits. However, with the huge volume of existing and newly-published books, it is a challenge for parents/educators (young readers, respectively) to find the right ones that match children's interests and their read-ability levels. In response to the needs, we have developed K3Rec, a recommender which applies a multi-dimensional approach to suggest books that simultaneously match the interests/preferences and reading abilities of emergent (i.e., K-3) readers. K3Rec considers the grade levels, contents, illustrations, and topics, besides using special properties, such as length and writing style, to distinguish K-3 books from other books targeting more mature readers. K3Rec is novel, since it adopts an unsupervised strategy to suggest books for K-3 readers which does not rely on the existence of personal social media data, such as personal tags and ratings, that are seldom, if ever, created by emergent readers. Further-more, unlike existing book recommenders, K3Rec explicitly analyzes book illustrations, which is of special significance for emergent readers, since illustrations assist these readers in understanding the contents of books. K3Rec focuses on a niche group of readers that has not been explicitly targeted by existing book recommenders. Empirical studies conducted using data from BiblioNasium.com and Amazon's Mechanical Turk have verified the effectiveness of K3Rec in making book recommendations for emergent readers.

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Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction