Buschek, DanielJust, IngoFritzsche, BenjaminAlt, FlorianDiefenbach, SarahHenze, NielsPielot, Martin2017-11-222017-11-222015978-3-11-044392-9https://dl.gi.de/handle/20.500.12116/7901Humoristic 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.HumorRecommender SystemsWorld Wide WebText/Conference Paper