Movies are made with the intent of evoking an emotional response. In recent years, researchers have hypothesized that the emotional response evoked by a movie can be leveraged to augment recommender system algorithms. Some researchers have leveraged the content of such movie reviews to develop an eight-dimensional emotional vector describing every movie on each of the eight emotions of Plutchik’s wheel of emotions. This eight-dimensional vector represents the “emotional signature” of the movie.
We further developed a novel movie recommender system that diversifies and visualizes its recommendations based on the emotional signature of movies. Additionally, we provided users with the ability to engage with the system by tweaking their emotion preference for movies.

We evaluated the motion diversification method, visualization, and the interactivity feature through an online user study following a user-centric evaluation approach. The results demonstrate that introducing emotion as an item attribute for diversification, visualization, and interaction has a significant effect on the user experience and supporting self-actualization.
 

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