Arthur da Costa Fortes

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In this paper, we propose a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user . Our approach is a response to the actual scenario on the Web which allows users to interact with the content in different ways, and thus, more information about his preferences can be obtained to(More)
In this paper, we present a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user. Our approach is a response to the actual scenario on the Web which allows users to interact with the content in different ways, and thus, more information about his preferences can be obtained to(More)
Recommendation of textual documents requires indexing mechanisms to extract structured metadata for attribute-aware recommender systems. Applying a variety of text mining algorithms has the advantage of capturing different aspects of unstructured content, resulting in richer descriptions. However, it is difficult to integrate them into a unique model so(More)
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