Applying Semantic Web and DBPEDIA to Recommender System

• Main Authors: Li Kai Yeh, 葉力愷
• Other Authors: J. C. Wang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107CGU05396033%22.&searchmode=basic

碩士 === 長庚大學 === 資訊管理學系 === 107 === Recommender system plays a big role in the society these days. In the Market it has a pivotal position. Academically, it has been a popular topic for researchers. There are more and more usable data because of the rapid growth of technology and the integrity of the corporation’s data. The customers can also rate projects or give some subjective feedback which also makes more usable data. How these data can be used is the most difficult subject. Recommender system is one of the method that can wisely use these usable data. Recommender system has a wide range of applications such as movies, music, news, travels and so on. This widely property has made recommender system so popular no matter in researches or businesses. To make the data more completed we brought in the concept of semantic web while extracting movies’ features from the linked open data(LOD) which is DBPEDIA. Finally, we build a model with similarity analyze and to predict the precision as 0.71. In this study we will focus on a movie recommender system based on a dataset from Movielens.