Tag-based Semantic Website Recommendation for Turkish Language

TL;DR

This paper presents a tag-based website recommendation system for Turkish language, combining similarity measures with semantic relationships of tags, evaluated through a user experiment in Turkey.

Contribution

It introduces a novel tag-based recommendation method that incorporates semantic relationships specifically tailored for Turkish language websites.

Findings

The system effectively recommends relevant Turkish websites.

Participants found the recommendations useful and accurate.

The approach improves upon traditional tag similarity methods.

Abstract

With the dramatic increase in the number of websites on the internet, tagging has become popular for finding related, personal and important documents. When the potentially increasing internet markets are analyzed, Turkey, in which most of the people use Turkish language on the internet, found to be exponentially increasing. In this paper, a tag-based website recommendation method is presented, where similarity measures are combined with semantic relationships of tags. In order to evaluate the system, an experiment with 25 people from Turkey is undertaken and participants are firstly asked to provide websites and tags in Turkish and then they are asked to evaluate recommended websites.