PLDSD

Abstract

A vast amount of data that can be easily read by machines have been published in freely accessible and interconnected datasets, creating the so-called Linked Open Data cloud. This phenomenon has opened opportunities for the development of semantic applications, including recommender systems. In this paper, we propose Personalized Linked Data Semantic Distance (PLDSD), a novel similarity measure for linked data that personalizes the RDF graph by adding weights to the edges, based on previous user's choices. Thus, our approach has the purpose of minimizing the sparsity problem by ranking the best features for a particular user, and also, of solving the item cold-start problem, since the feature ranking task is based on features shared between old items and the new item. We evaluate PLDSD in the context of a LOD-based Recommender System using mixed data from DBpedia and MovieLens, and the experimental results indicate better accuracy of recommendations compared to a non-personalized baseline similarity method.