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Recommendation systems have extracted items that users may be interested in. However, most of the recommendation applications have restricted on recommending only items in a specific domain (e.g., movies, books, and musics). In this paper, we propose a novel approach to enable the existing recommendation systems to extract addition items in various other domains. Thereby, this work is focusing on integrating all available Linked Open Data (LOD) for selecting more relevant attributes which can improve the performance of recommendation processes.
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