TY - JOUR AU - 김상욱 DA - 2015/01 PY - 2015 SN - 978-145033377-1 UR - http://hdl.handle.net/20.500.11754/21467 UR - http://dl.acm.org/citation.cfm?doid=2701126.2701205 AB - 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. PB - ACM ICUIMC KW - Recommendation Method KW - News Articles, KW - Latest News Articles KW - MinHash KW - LSH TI - A Method for Recommending the Latest News Articles via MinHash and LSH DO - 10.1145/2701126.2701205 ER -