Humoristic content is an inherent part of the World Wide Web and increasingly consumed for micro-entertainment. However, humor is often highly individual and depends on background knowledge and context. This paper presents an approach to recommend humoristic content fitting each individual user's taste and interests. In a field study with 150 participants over four weeks, users rated content with a 0-10 scale on a humor website. Based on this data, we train and apply a Collaborative Filtering (CF) algorithm to assess individual humor and recommend fitting content. Our study shows that users rate recommended content 22.6% higher than randomly chosen content.
