Automating Readers ’ Advisory to Make Book Recommendations for K-12 Readers

The academic performance of students is affected by their reading ability, which explains why reading is one of the most important aspects of school curriculums. Promoting good reading habits among K-12 students is essential, given the enormous influence of reading on students ’ development as learners and members of society. In doing so, it is in-dispensable to provide readers with engaging and motivat-ing reading selections. Unfortunately, existing book recom-menders have failed to offer adequate choices for K-12 read-ers, since they either ignore the reading abilities of their users or cannot acquire the much-needed information to make recommendations due to privacy issues. To address these problems, we have developed Rabbit, a book recom-mender that emulates the readers ’ advisory service offered at school/public libraries. Rabbit considers the readability levels of its readers and determines the facets, i.e., appeal factors, of books that evoke subconscious, emotional reac-tions on a reader. The design of Rabbit is unique, since it adopts a multi-dimensional approach to capture the reading abilities, preferences, and interests of its readers, which goes beyond the traditional book content/topical analysis. Con-ducted empirical studies have shown that Rabbit outper-forms a number of (readability-based) book recommenders. Categories and Subject Descriptors Information Systems [Retrieval tasks and goals]: [Rec-ommender systems