Recommendation system based on multilingual entity matching on linked open data

Since we have been facing multilingual contents, it is difficult for recommender systems (RecSys) to efficiently collect user feedbacks (e. g., ratings). Thus, we expect that multilingual entities matching can improve the performance of recommendation services. Particularly, in movie recommendation services, the movies have several titles in different languages. Thereby, we are focusing on interlinking some possible data sources including traditional tabular data (e. g., IMDB) and Linked Open Data (LOD) (e. g., DBpedia and LinkedMDB). This paper shows meaningful experiences that we have observed during experimentation; i) discovering identical movies which have multilingual titles by interlinking LOD, and ii) improving the performance of multilingual recommendation.