A New Hybrid Popular Model for Personalized Tag Recommendation

Abstract

Tagging systems have been playing an important role in many websites during the web2.0 age. Users use a social tagging system to effectively mark web resources with personalized tags, which can help them organize and share their items easily. In this work, we put forward a new model IBHP to recommend personalized tags for users. We evaluate our model on a real-world dataset collected on Delicious 1 . Data tests show that our model can get better performance than currently widely used popularity-based methods, which also use the same available information: <user, item, tag> ternary relations.