An Introduction of a Tag Ratio Model and the Classification Examination for Recommender Systems
Kazuki Yamauchi, Naruaki Toma, Yuhei Akamine, Kôji Yamada, Satoshi Endo · Procedia Computer Science · 2013
Tagging has emerged as a powerful mechanism that enables users to find and understand entities. However, there are three types of issues in traditional tagging systems. In this paper, we explore and seek a tag-algorithm that predicts tags of users and contents with a degree of relevance, which we called the tag ratio. We described our algorithm and evaluated them by the Naive Bayes classifier. Experiment results showed that all rating's precision of sought continuous number content's tag were better than raw binary valued content's tag's.