|Title||Entity Recommendations Using Hierarchical Knowledge Bases|
|Publication Type||Conference Proceedings|
|Year of Publication||2015|
|Authors||Siva Cheekula, Pavan Kapanipathi, Derek Doran, Prateek Jain, Amit Sheth|
|Conference Name||Extended Semantic Web Conference 2015 (ESWC 2015)|
|Conference Location||Portoroz, Slovenia|
|Keywords||Content-based recommendations, Entity Relationships, Hierarchy, Knowledge Bases, Semantics, Wikipedia|
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.