Using Graph Metrics for Linked Open Data Enabled Recommender Systems

Petar Ristoski; Michael Schuhmacher; Heiko Paulheim

doi:10.1007/978-3-319-27729-5_3

ScienceGate Book Chapters

BOOK-CHAPTER

Using Graph Metrics for Linked Open Data Enabled Recommender Systems

Petar Ristoski Michael Schuhmacher Heiko Paulheim

Year: 2015 Lecture notes in business information processing Pages: 30-41 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-319-27729-5_3

Get Full-Text PDF Get Analytical Report

Keywords:

Recommender system Computer science Collaborative filtering Graph Relevance (law) Information retrieval Domain (mathematical analysis) Data mining Theoretical computer science Mathematics

Metrics

Cited By

1.10

FWCI (Field Weighted Citation Impact)

Refs

0.79

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Recommender Systems and Techniques

Physical Sciences → Computer Science → Information Systems

Advanced Graph Neural Networks

Physical Sciences → Computer Science → Artificial Intelligence

Topic Modeling

Physical Sciences → Computer Science → Artificial Intelligence

Using Graph Metrics for Linked Open Data Enabled Recommender Systems

Metrics

Citation History

Topics

Related Documents

Introducing linked open data in graph-based recommender systems

Using Linked Open Data in Recommender Systems

Measuring semantic distance for linked open data-enabled recommender systems

Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data

SemStim at the Linked Open Data-enabled Recommender Systems 2014 challenge