The document discusses evaluating semantic patterns for recommendations based on crowdsourced feedback. It tested recommendations generated from DBpedia patterns against Amazon recommendations. Participants chose a movie to accompany the movie "Lincoln" from options. Statistics on the patterns like frequency and length were analyzed to see which best correlated with user choices. Global frequency and pattern length were found to correlate highest with improving diversity in recommendations.