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Recommender systems arose from the need to select and present relevant content to users according to their preferences. Among several existent methods, those based on content make exclusive use of information inherent to the items. This information can be created through automatic and manual indexing techniques. While automatic approaches require greater computing resources and are limited to the specific task they perform, manual methods are expensive and prone to errors. On the other hand, with the expansion of the Web and the possibility of common users to create new content and descriptions about different items and products, an alternative is to get these metadata created collaboratively by the users. However, this information, especially reviews and comments, may contain noise, besides being in a unstructured fashion. Thus, this study aims to develop methods for the construction of itemsâ? representations based on collaborative descriptions for a recommender system. This study aims to analyze the impact that different feature extraction techniques, combined with sentiment analysis, caused in the accuracy of the generated suggestions, evaluating the results in both recommendation scenarios: rating prediction and ranking generation. Among the analyzed techniques, it is observed that the best is able to describe items in a more efficient manner, resulting in an improvement in the recommendation system. Keyword: Recommender systems, unstructured information, items’ representation, feature extraction, sentiment analysis.

Filtragem baseada em conteúdo auxiliada por métodos de indexação colaborativa