Entity Recommendations Using Hierarchical Knowledge Bases

Author(s)

Siva Kumar Cheekula
Pavan Kapanipathi
Derek Doran
Prateek Jain
Amit P. Sheth, University of South Carolina-ColumbiaFollow

Document Type

Conference Proceeding

Abstract

Recent developments in recommendation algorithms have focused on integrating Linked Open Data to augment traditional algorithms with background knowledge. These developments recognize that the integration of Linked Open Data may or better performance, particularly in cold start cases. In this paper, we explore if and how a specific type of Linked Open Data, namely hierarchical knowledge, may be utilized for recommendation systems. We propose a content-based recommendation approaches that adapts a spreading activation algorithm over the DBpedia category structure to identify entities of interest to the user. Evaluation of the algorithm over the Movielens dataset demonstrates that our method yields more accurate recommendations compared to a previously proposed taxonomy driven approach for recommendations.

Publication Info

Published in CEUR Workshop Proceedings, Issue 1365, 2015.

© CEUR Workshop Proceedings, 2015

APA Citation

Cheekula, S. K., Kapanipathi, P., Doran, D., Jain, P., & Sheth, A. P. (2015). Entity Recommendations Using Hierarchical Knowledge Bases. CEUR Workshop Proceedings, 1365.
https://corescholar.libraries.wright.edu/knoesis/1062