PADAC⁁2: Real-time news recommendation system with heterogeneous social footprints

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 PADAC^Λ2, 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.