Enhancing Recommender System with Linked Open Data
In this paper, we present an innovative method to use Linked Open Data (LOD) to improve content based recommender systems. We have selected the domain of secondhand bookshops, where recommending is extraordinary difficult because of high ratio of objects/users, lack of significant attributes and small number of the same items in stock. Those difficulties prevents us from successfully apply both collaborative and common content based recommenders. We have queried Czech language mutation of DBPedia in order to receive additional attributes of objects (books) to reveal nontrivial connections between them. Our approach is general and can be applied on other domains as well. Experiments show that enhancing recommender system with LOD can significantly improve its results in terms of object similarity computation and top-k objects recommendation. The main drawback hindering widespread of such systems is probably missing data about considerable portion of objects, which can however vary across domains and improve over time.
KeywordsRecommender systems Linked Open Data implicit user preference content based similarity
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