Top-N Recommendations from Implicit Feedback leveraging Linked Open Data
Authors
Ostuni Vito Claudio, Di Noia Tommaso, Di Sciascio Eugenio, Mirizzi RobertoAbstract
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.
DOI
https://doi.org/10.1145/2507157.2507172BibTex references
@InProceedings{ODDM13, author = "Ostuni, Vito Claudio and Di Noia, Tommaso and Di Sciascio, Eugenio and Mirizzi, Roberto", title = "Top-N Recommendations from Implicit Feedback leveraging Linked Open Data", booktitle = "7th ACM Conference on Recommender Systems (RecSys 2013)", year = "2013", publisher = "ACM Press", organization = "ACM", url = "http://sisinflab.poliba.it/Publications/2013/ODDM13" }