Renato Dompieri Beltrao

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This paper proposes a study and comparison of the combination of multiple metadata types to improve the recommendation of movie items according to users' preferences. We used four algorithms available in the literature to analyze the descriptions, and compared each other using all the possible combinations of the metadata extracted from two datasets, namely(More)
In this paper, we analyze the application of ensemble algorithms to improve the ranking recommendation problem with multiple metadata. We propose three generic ensemble strategies that do not require modification of the recommender algorithm. They combine predictions from a recommender trained with distinct metadata into a unified rank of recommended items.(More)
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