  We apply the concept of users' emotion vectors (UVECs) and movies' emotion
vectors (MVECs) as building components of Emotion Aware Recommender System. We
built a comparative platform that consists of five recommenders based on
content-based and collaborative filtering algorithms. We employed a Tweets
Affective Classifier to classify movies' emotion profiles through movie
overviews. We construct MVECs from the movie emotion profiles. We track users'
movie watching history to formulate UVECs by taking the average of all the
MVECs from all the movies a user has watched. With the MVECs, we built an
Emotion Aware Recommender as one of the comparative platforms' algorithms. We
evaluated the top-N recommendation lists generated by these Recommenders and
found the top-N list of Emotion Aware Recommender showed serendipity
recommendations.
