Entity Recommendations Using Hierarchical Knowledge Bases

TitleEntity Recommendations Using Hierarchical Knowledge Bases
Publication TypeConference Proceedings
Year of Publication2015
AuthorsSiva Cheekula, Pavan Kapanipathi, Derek Doran, Prateek Jain, Amit Sheth
Conference NameExtended Semantic Web Conference 2015 (ESWC 2015)
Date Published05/2015
Conference LocationPortoroz, Slovenia
KeywordsContent-based recommendations, Entity Relationships, Hierarchy, Knowledge Bases, Semantics, Wikipedia
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.

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