Camila Vaccari Sundermann

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In this paper, we propose a multi-view based metadata extraction technique from unstructured textual content in order to be applied in recommendation algorithms based on latent factors. The solution aims at reducing the problem of intense and time-consuming human effort to identify, collect and label descriptions about the items. Our proposal uses a(More)
Unlike traditional recommender systems, which make recommendations only by using the relation between users and items, a context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process. One problem of context-aware approaches is that it is required techniques to extract such additional(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)
—A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user. Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available(More)
Text clustering is a text mining task which is often used to aid the organization, knowledge extraction, and exploratory search of text collections. Nowadays, the automatic text clustering becomes essential as the volume and variety of digital text documents increase, either in social networks and the Web or inside organizations. This paper explores the use(More)
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