• Àüü
  • ÀüÀÚ/Àü±â
  • Åë½Å
  • ÄÄÇ»ÅÍ
´Ý±â

»çÀÌÆ®¸Ê

Loading..

Please wait....

±¹³» ÇÐȸÁö

Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ÇÐȸÁö > µ¥ÀÌÅͺ£À̽º ¿¬±¸È¸Áö(SIGDB)

µ¥ÀÌÅͺ£À̽º ¿¬±¸È¸Áö(SIGDB)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) »ç¿ëÀÚÀÇ ±àÁ¤ÀûÀÎ ¿µÇâ·ÂÀ» ÀÌ¿ëÇÑ Ãßõ ±â¹ý
¿µ¹®Á¦¸ñ(English Title) Recommendation Technique based on User¡¯s Positive Influence
ÀúÀÚ(Author) Jin Zhanying   ¿À¼öÁ¤   ±èº¸Çö   À̹μö   Zhanying Jin   Sujoung Oh   Bohyun Kim   Minsoo Lee  
¿ø¹®¼ö·Ïó(Citation) VOL 31 NO. 01 PP. 0029 ~ 0039 (2015. 04)
Çѱ۳»¿ë
(Korean Abstract)
½º¸¶Æ®Æù°ú Ŭ¶ó¿ìµå ÄÄÇ»ÆÃÀÇ º¸ÆíÈ­¿Í ¼Ò¼È ³×Æ®¿öÅ©ÀÇ ºó¹øÇÑ »ç¿ëÀ¸·Î »õ·Î »ý¼ºµÇ´Â µ¥ÀÌÅÍÀÇ ¾çÀÌ ³¯¸¶´Ù Áõ°¡ÇÏ°í ÀÖ´Â °¡¿îµ¥ ºÒÇÊ¿äÇÑ Á¤º¸¸¦ ÇÊÅ͸µÇÏ°í »ç¿ëÀÚ¿¡°Ô ÀûÇÕÇÑ Á¤º¸¸¦ Á¦°øÇÏ´Â Ãßõ½Ã½ºÅÛÀÌ ¸¹ÀÌ Á¦¾ÈµÇ°í ÀÖ´Ù. º» ³í¹®¿¡¼­´Â ¼Ò¼È ³×Æ®¿öÅ©¿¡¼­ÀÇ »ç¿ëÀÚÀÇ Æ¯Á¤ Ä«Å×°í¸®¿¡ ´ëÇÑ Àû±Ø¼º, Àü¹®¼º, »ç¿ëÀÚ°¡ ´Ù¸¥ »ç¿ëÀÚµé·ÎºÎÅÍ °ü½ÉÀ» ¹Þ´Â ¼ö¿Í ±àÁ¤ÀûÀÎ Æò°¡¿Í °°Àº ¿ä¼ÒµéÀ» °í·ÁÇÏ¿© À̵鿡 ´ëÇÑ ±¸Ã¼ÀûÀÎ È°µ¿¿ä¼Ò·Î ÀÌ·ç¾îÁø °ªÀ» °è»êÇÏ¿© °¡Àå ¿µÇâ·Â ÀÖ´Â »ç¿ëÀÚ¸¦ ã¾Æ³½ ÈÄ ±× »ç¿ëÀÚ°¡ ³ô°Ô Æò°¡ÇÑ ¾ÆÀÌÅÛÀ» ÃßõÇÏ´Â ¹æ¹ý¿¡ ´ëÇØ Á¦¾ÈÇÏ°íÀÚ ÇÑ´Ù.
¿µ¹®³»¿ë
(English Abstract)
As smart phones and cloud computing are widely used and social networks are frequently being used, the amount of new data is increasing every day, and therefore many recommendation systems that filter out useless information and provide suitable information to users are being proposed. In this work, within the social network, we consider the user¡¯s motivation of the specific category, the level of expertise, the number of followers and the compliments that are written by other users, and calculate a value based on real activity elements to find the most influential user. We propose that the items that are highly evaluated by such an influential user may be used for recommendation.
Å°¿öµå(Keyword) ¼Ò¼È ³×Æ®¿öÅ©   »ç¿ëÀÚ ¿µÇâ·Â   È°µ¿ À̷   social network   user influence   activity history  
ÆÄÀÏ÷ºÎ PDF ´Ù¿î·Îµå