Machine Learning
Generating semantically enriched user profiles for Web personalization
ACM Transactions on Internet Technology (TOIT)
A multilayer ontology-based hybrid recommendation model
AI Communications - Recommender Systems
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Knowledge infusion into content-based recommender systems
Proceedings of the third ACM conference on Recommender systems
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
BagBoo: a scalable hybrid bagging-the-boosting model
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
A generic semantic-based framework for cross-domain recommendation
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
SLIM: Sparse Linear Methods for Top-N Recommender Systems
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Linked open data to support content-based recommender systems
Proceedings of the 8th International Conference on Semantic Systems
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
Proceedings of the sixth ACM conference on Recommender systems
Sparse linear methods with side information for top-n recommendations
Proceedings of the sixth ACM conference on Recommender systems
Exploiting the web of data in model-based recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Efficient top-n recommendation for very large scale binary rated datasets
Proceedings of the 7th ACM conference on Recommender systems
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The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper we present SPrank, a novel hybrid recommendation algorithm able to compute top-N item recommendations from implicit feedback exploiting the information available in the so called Web of Data. We leverage DBpedia, a well-known knowledge base in the LOD compass, to extract semantic path-based features and to eventually compute recommendations using a learning to rank algorithm. Experiments with datasets on two different domains show that the proposed approach outperforms in terms of prediction accuracy several state-of-the-art top-N recommendation algorithms for implicit feedback in situations affected by different degrees of data sparsity.