News Recommendation based on Human Similarity Judgment
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- Master theses 
Similar item recommendation is one of the most popular types of recommender systems. As the name implies, the objective is to recommend items that are similar to a reference item. The news domain is one of the many that employ this form of recommendation, which utilize similarity functions in order to calculate the similarity. In this study, human judgments of article similarity were acquired using an online user study in which each of the 173 participants evaluated the similarity of 12 pairs of articles. Each of the 12 article pairs had their own unique characteristics. One pair would be made up of two completely dissimilar articles, while the other pairs had either a shared topic, a named entity in common, publication dates in close proximity, or some combination of these three characteristics. Half the pairs contained articles from the News (i.e., recent events) category, while the other half contained Sport articles. The similarity of the same article pairings was then calculated utilizing various similarity functions, and the correlation between human judgment and function scores was computed. This thesis found that the correlation ranged from weak to strong, depending on the function. The thesis also found that the correlation is largely dependent on the whether the articles have certain characteristics in common. On average, the functions correlated more strongly to human judgment if the articles belonged the category News (i.e., recent events) than Sport. The functions were also better at predicting human similarity when the articles in question were relatively similar to one another. The novel work presented in this thesis shows that the correlation between human judgment and similarity functions can be stronger than previous work has suggested, if news articles are paired in a meaningful way.