SKOS-Based Concept Expansion for LOD-Enabled Recommender Systems.

摘要: This paper presents a concept expansion strategy for Linked Open Data-enabled recommender systems (LDRS). is based on annotations from Simple Knowledge Organization System (SKOS) vocabularies. To this date, the knowledge structures of SKOS graphs have not yet been thoroughly explored item similarity calculation in content-based (RS). While some researchers already performed an unweighted skos:broader links, quantification relatedness concepts with quality issues, such as DBpedia category system, should be further investigated to improve recommendation results. For purpose, we apply our approach conjunction suitable concept-to-concept metric and test it three different LDRS datasets multimedia domain (i.e., movie, music book RS). The results showed that has diversifying effect result lists, while at least providing same level accuracy system running non-expansion mode.