Automating readers' advisory to make book recommendations for K-12 readers

Maria Soledad Pera*, Yiu Kai Ng

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

33 Citations (Scopus)


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 indispensable to provide readers with engaging and motivating reading selections. Unfortunately, existing book recommenders have failed to offer adequate choices for K-12 readers, 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 recommender 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 reactions 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. Conducted empirical studies have shown that Rabbit outperforms a number of (readability-based) book recommenders.

Original languageEnglish
Title of host publicationRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Electronic)9781450326681
Publication statusPublished - 6 Oct 2014
Externally publishedYes
Event8th ACM Conference on Recommender Systems, RecSys 2014 - Foster City, United States
Duration: 6 Oct 201410 Oct 2014


Conference8th ACM Conference on Recommender Systems, RecSys 2014
Country/TerritoryUnited States
CityFoster City


  • Books
  • K-12
  • Readers' advisory
  • Recommendation system


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