Generating Recommendations Based on Robust Term Extraction from Users' Reviews

In this paper, we propose a technique to automatically describe items based on users' reviews in order to be used by recommender systems. For that, we extract items' features using a robust term extraction method that applies transductive semi-supervised learning to automatically identify aspects that represent the different subjects of the reviews. Then, we apply sentiment analysis in a sentence level to indicate the polarities, yielding a consensus of users regarding the features of items. Our approach is evaluated using a collaborative filtering method, and comparisons using structured metadata as baselines show promising results.