Recommending Books for Children Based on the Collaborative and Content-Based Filtering Approaches

According to a study conducted by the National Institute of Child Health and Human Development, reading is the single most important skill necessary for a happy, productive, and successful life. A child who is an excellent reader often has confident and a high level of self esteem and can easily make the transition from learning to read to reading to learn. Promoting good reading habits among children is essential, given the enormous influence of reading on students’ development as learners and members of the society. Unfortunately, very few (children) websites or online applications recommend books to children, even though they can play a significant role in encouraging children to read. Popular book websites, such as goodreads.com, commonsensemedia.org, and readanybook.com, suggest books to children based on the popularity of books or rankings on books, which are not customized/personalized for each individual user and likely recommend books that users do not want or like. We have integrated the collaborative filtering (CF) approach and the content-based approach, in addition to predicting the grade levels of books, to recommend books for children. The user-based CF approaches filter books appealing to each user based on users’ ratings, whereas the content-based filtering method analyzes the descriptions of books rated by a user in the past and constructs a user profile to capture the user’s preferences. Recent research works have demonstrated that a hybrid approach, which combines the content-based filtering and CF approaches is more effective in making recommendations. Conducted empirical study has verified the effectiveness of our proposed children book recommender.

[1]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[2]  ZhiPing Ding.  The Development of Ontology Information System Based on Bayesian Network and Learning , 2011 .

[3]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[4]  Allison Woodruff,et al.  Enhancing a digital book with a reading recommender , 2000, CHI.

[5]  Pável Calado,et al.  Improving a hybrid literary book recommendation system through author ranking , 2012, JCDL '12.

[6]  You-Jin Park,et al.  Individual and group behavior-based customer profile model for personalized product recommendation , 2009, Expert Syst. Appl..

[7]  Victor Lavrenko,et al.  Predicting social-tags for cold start book recommendations , 2009, RecSys '09.

[8]  Kate Cain,et al.  The Precursors of Reading Ability in Young Readers: Evidence From a Four-Year Longitudinal Study , 2012 .

[9]  Zhi-Dan Zhao,et al.  User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[10]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[11]  Binge Cui,et al.  An Online Book Recommendation System Based on Web Service , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[12]  C. Juel.  Learning to read and write: A longitudinal study of 54 children from first through fourth grades. , 1988 .

[13]  Chun Chen,et al.  Document recommendation in social tagging services , 2010, WWW '10.

[14]  Maria Soledad Pera,et al.  What to read next?: making personalized book recommendations for K-12 users , 2013, RecSys.

[15]  Maria Soledad Pera,et al.  With a Little Help from My Friends: Generating Personalized Book Recommendations Using Data Extracted from a Social Website , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[16]  R. Allington,et al.  Every Child, Every Day , 2012 .

[17]  Jun Wang,et al.  Unifying user-based and item-based collaborative filtering approaches by similarity fusion , 2006, SIGIR.

[18]  Baogang Wei,et al.  CARES: a ranking-oriented CADAL recommender system , 2009, JCDL '09.

[19]  Marcos André Gonçalves,et al.  A source independent framework for research paper recommendation , 2011, JCDL '11.

[20]  Robin Burke,et al.  Improving the effectiveness of collaborative recommendation with ontology-based user profiles , 2010, HetRec '10.

[21]  Saroj Koul,et al.  Review of Digital Library Book Recommendation Models , 2009 .

[22]  M. Stuart.  Learning to read: A longitudinal study , 1993 .

[23]  Readability and Reading Ability. , 1998 .

[24]  Xiaotian Chen,et al.  Google Books and WorldCat: a comparison of their content , 2012 .