Joke Recommender System Using Humor Theory
In this paper, we propose a methodology that aims to develop a recommendation system for jokes by analyzing its text. This exploratory study focuses mainly on the General Theory of Verbal Humor and implements the knowledge resources defined by it to annotate the jokes. These annotations contain the characteristics of the jokes and hence are used to determine how alike the jokes are. We use Lin’s similarity metric and Word2vec to calculate the similarity between different jokes. The jokes are then clustered hierarchically based on their similarity values for the recommendation. Finally, for multiple users, we compare our joke recommendations to those obtained by the Eigenstate algorithm which does not consider the content of the joke in its recommendation.