Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A novel collaborative filtering approach for recommending ranked items
Expert Systems with Applications: An International Journal
A new method to measure the semantic similarity of GO terms
Bioinformatics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A knowledge-based product recommendation system for e-commerce
International Journal of Intelligent Information and Database Systems
A New Measure Based on Gene Ontology for Semantic Similarity of Genes
ICIE '10 Proceedings of the 2010 WASE International Conference on Information Engineering - Volume 01
Engineering Applications of Artificial Intelligence
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Making personalized recommendation according to preferences of users is of great importance in recommender systems. Currently most book recommender systems take advantage of relational databases for the representation of knowledge and depend on historical data for the calculation of relationships between books. This scheme, though having been widely used in existing methods based on the collaborative filtering strategy, overlooks intrinsic semantic relationships between books. To overcome this limitation, we propose a novel approach called COSEY (COllaborative filtering based on item SEmantic similaritY) to achieve personalized recommendation of books. We derive semantic similarities between books based on semantic similarities between concepts in an ontology that describes categories of books using our previously proposed method DOPCA, and we incorporate such similarities between books into the item-based collaborative filtering strategy to achieve personalized recommendation. We validate the proposed COSEY approach through comprehensive experiments and show the superior performance of this approach over existing methods in the recommendation of books.