PADAC⁁2: Real-time News Recommendation System with Heterogeneous Social Footprints
Dong-Yup Kang, Dong-Kyun Han, Gyumin Sim, Jong Hyuk Jung, Hyun Ki-Jeon, Soobin Lee, Joonyoung Park, Seunghyeon Moon
Everyday in Korea, more than hundred thousands of News articles and postings are generated by either writers or users. Many people read News articles and write their opinions on the articles through major News portal systems such as Naver or Daum. However, they are sometimes time-consuming, biased, and distracted by unnecessary information. We propose a realtime News recommendation system called PADACΛ2, that is more passive process for users to browse their interests from massive News media. We propose a recommendation algorithm called HeteRoCommender based on heterogeneous source of social footprints given.
I mainly took a part of designing user interfaces of app-based research tools and partially participated in developing web-based tools to answer our defined research questions and prove the hyptotheses.
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