Top-N Recommendations from Implicit Feedback leveraging Linked Open Data

7th ACM Conference on Recommender Systems (RecSys 2013) - -2013

Authors

Ostuni Vito Claudio, Di Noia Tommaso, Di Sciascio Eugenio, Mirizzi Roberto

Abstract

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.

Download: rec105-ostuni.pdf

DOI

https://doi.org/10.1145/2507157.2507172

BibTex 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"

}