Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Journal of Machine Learning Research
Individual and group behavior-based customer profile model for personalized product recommendation
Expert Systems with Applications: An International Journal
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
CARES: a ranking-oriented CADAL recommender system
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Cognitively motivated features for readability assessment
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Predicting social-tags for cold start book recommendations
Proceedings of the third ACM conference on Recommender systems
Document recommendation in social tagging services
Proceedings of the 19th international conference on World wide web
Improving the effectiveness of collaborative recommendation with ontology-based user profiles
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Recommender Systems Handbook
A source independent framework for research paper recommendation
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Fusing Recommendations for Social Bookmarking Web Sites
International Journal of Electronic Commerce
ReadAid: A Robust and Fully-Automated Readability Assessment Tool
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Using word clusters to detect similar web documents
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Recommender Systems for Learning
Recommender Systems for Learning
Ranking-based readability assessment for early primary children's literature
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Finding books that children/teenagers are interested in these days is a non-trivial task due to the diversity of topics covered in huge volumes of books with varied readability levels. Even though K-12 readers can turn to book recommenders to look for books, the recommended books may not satisfy their personal needs, since they could be beyond/below their readability levels or fail to match their topics of interest. To address these problems, we introduce BReK12, a book recommender that makes personalized suggestions tailored to each K-12 user U based on books available on a social book-marking site that (i) are similar in content to the ones that are known to be of interest to U, (ii) have been bookmarked by users with reading patterns similar to U's, and (iii) can be comprehended by U. BReK12 is an asset to its users, since it suggests books that are appealing to its users and at grade levels that they can cope with, which can increase their reading selection choices and motivate them to read. We have also developed ReLAT, the readability analysis tool employed by BReK12 to determine the grade level of books. ReLAT is novel, compared with existing readability formulas, since it can predict the grade level of a book even if an excerpt of the book is not available. We have conducted empirical studies which have verified the accuracy of ReLAT in predicting the grade level of a book and the effectiveness of BReK12 over existing baseline recommendation systems.