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- W2294820775 abstract "In this paper we compare several techniques to automatically feed a graph-based recommender system with features extracted from the Linked Open Data (LOD) cloud. Specifically, we investigated whether the integration of LOD-based features can improve the e↵ectiveness of a graph-based recommender system and to what extent the choice of the features selection technique can influence the behavior of the algorithm by endogenously inducing a higher accuracy or a higher diversity. The experimental evaluation showed a clear correlation between the choice of the feature selection technique and the ability of the algorithm to maximize a specific evaluation metric. Moreover, our algorithm fed with LODbased features was able to overcome several state-of-the-art baselines: this confirmed the e↵ectiveness of our approach and suggested to further investigate this research line." @default.
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- W2294820775 date "2015-01-01" @default.
- W2294820775 modified "2023-09-25" @default.
- W2294820775 title "Automatic selection of linked open data features in graph-based recommender systems" @default.
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