An Introduction of a Tag Ratio Model and the Classification Examination for Recommender Systems | Zendy

Kazuki Yamauchi | Zendy; Naruaki Toma | Zendy; Yuhei Akamine | Zendy; Kôji Yamada | Zendy; Satoshi Endo | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

An Introduction of a Tag Ratio Model and the Classification Examination for Recommender Systems

Author(s) -

Kazuki Yamauchi,

Naruaki Toma,

Yuhei Akamine,

Kôji Yamada,

Satoshi Endo

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.09.141

Subject(s) - computer science , recommender system , naive bayes classifier , classifier (uml) , information retrieval , binary classification , binary number , artificial intelligence , data mining , machine learning , support vector machine , arithmetic , mathematics

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

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research