Top-N Recommendations from Implicit Feedback leveraging Linked Open Data

7th ACM Conference on Recommender Systems (RecSys 2013) - 2013
Download the publication : rec105-ostuni.pdf [798Ko]  
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.

BibTex references

author = {Vito Claudio Ostuni and Tommaso {Di Noia} and Eugenio {Di Sciascio} and Roberto Mirizzi},
title = "Top-N Recommendations from Implicit Feedback
leveraging Linked Open Data",
booktitle = "7th ACM Conference on Recommender Systems (RecSys
year = "2013",
publisher = "ACM Press",
organization = "ACM",
url = "

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