Entity Recommendations Using Hierarchical Knowledge Bases

Citation data:

CEUR Workshop Proceedings, ISSN: 1613-0073, Vol: 1365

Publication Year:
2015
Usage 232
Downloads 152
Abstract Views 80
Repository URL:
https://corescholar.libraries.wright.edu/knoesis/1062
Author(s):
Cheekula, Siva Kumar; Kapanipathi, Pavan; Doran, Derek; Jain, Prateek; Sheth, Amit P.
Tags:
Content-Based Recommendations; Entity Relationships; Wikipedia; Semantics; Hierarchy; Knowledge Bases; Content-Based Recommendations; Entity Relationships; Wikipedia; Semantics; Hierarchy; Knowledge Bases; Bioinformatics; Communication; Communication Technology and New Media; Computer Sciences; Databases and Information Systems; Life Sciences; OS and Networks; Physical Sciences and Mathematics; Science and Technology Studies; Social and Behavioral Sciences
conference paper description
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.