SPRank: Semantic Path-based Ranking for Top-N Recommendations using Linked Open Data

ACM Transactions on Intelligent Systems and Technology (TIST) - -2016

Authors

Di Noia Tommaso, Ostuni Vito Claudio, Tomeo Paolo, Di Sciascio Eugenio

Abstract

In most real-world scenarios, the ultimate goal of recommender system applications is to suggest a short ranked list of items, namely top- N recommendations, that will appeal to the end user. Often, the problem of computing top- N recommendations is mainly tackled with a two-step approach. The system focuses first on predicting the unknown ratings, which are eventually used to generate a ranked recommendation list. Actually, the top- N recommendation task can be directly seen as a ranking problem where the main goal is not to accurately predict ratings but to directly find the best-ranked list of items to recommend. In this article we present SPrank, a novel hybrid recommendation algorithm able to compute top- N recommendations exploiting freely available knowledge in the Web of Data. In particular, we employ DBpedia, a well-known encyclopedic knowledge base in the Linked Open Data cloud, to extract semantic path-based features and to eventually compute top- N recommendations in a learning-to-rank fashion. Experiments with three datasets related to different domains (books, music, and movies) prove the effectiveness of our approach compared to state-of-the-art recommendation algorithms.

Download: SPRank Semantic Path-based Ranking for Top-N Recommendations using Linked Open Data - ACM TIST 2016.pdf

DOI

https://doi.org/10.1145/2899005

BibTex references

@Article{DOTD16,
  author       = "Di Noia, Tommaso and Ostuni, Vito Claudio and Tomeo, Paolo and Di Sciascio, Eugenio",
  title        = "SPRank: Semantic Path-based Ranking for Top-N Recommendations using Linked Open Data",
  journal      = "ACM Transactions on Intelligent Systems and Technology (TIST)",
  year         = "2016",
  note         = "to appear",
  url          = "http://sisinflab.poliba.it/Publications/2016/DOTD16"

}