Make Me Laugh: Recommending Humoristic Content on the WWW
نویسندگان
چکیده
Humoristic content is an inherent part of the World Wide Web and increasingly consumed for microentertainment. 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.
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