김상욱
2016-05-31T08:22:10Z
2016-05-31T08:22:10Z
2015-01
ACM IMCOM 2015 - Proceedings 8 January 2015, Article number a60, Page. 1-6
978-145033377-1
http://hdl.handle.net/20.500.11754/21467
http://dl.acm.org/citation.cfm?doid=2701126.2701205
Since most users are more interested in the latest news articles that are recently updated, it is important to recommend those news articles to appropriate users. However, existing methods cannot recommend the latest news articles in a short time. This paper proposes a novel recommendation method focusing on the latest news articles. It spends much shorter execution time than the existing methods thanks to employing two approximation methods, MinHash and locality sensitive hashing. For evaluation, we conducted extensive experiments using a real-world dataset. The experimental results show that our method provides better accuracy and performs much faster than the existing methods.
This work was supported by (1) Basic Science Research Program through National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (No. 2012R1A1A2007817), (2) the ICT R&D program of MSIP/IITP (14-824-09-001, Development of High Performance Visual BigData Discovery Platform for Large-Scale Realtime Data Analysis), and (3) Seoul Creative Human Development Program (HM120006).
en
ACM ICUIMC
Recommendation Method
News Articles,
Latest News Articles
MinHash
LSH
A Method for Recommending the Latest News Articles via MinHash and LSH
Article
10.1145/2701126.2701205
1-6
Hwang, W.-S.
Park, J.
Kim, S.-W.
20150072
S
COLLEGE OF ENGINEERING[S]
DIVISION OF COMPUTER SCIENCE AND ENGINEERING