Improving item recommendation based on social tag ranking

Content-based filtering is a popular framework for item recommendation. Typical methods determine items to be recommended by measuring the similarity between items based on the tags provided by users. However, because the usefulness of tags depends on the annotator's skills, vocabulary and feelings, many tags are irrelevant. This fact degrades the accuracy of simple content-based recommendation methods. To tackle this issue, this paper enhances content-based filtering by introducing the idea of tag ranking, a state-of-the-art framework that ranks tags according to their relevance levels. We conduct experiments on videos from a video-sharing site. The results show that tag ranking significantly improves item recommendation performance, despite its simplicity.