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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ ³í¹®Áö I : Á¤º¸Åë½Å

Á¤º¸°úÇÐȸ ³í¹®Áö I : Á¤º¸Åë½Å

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) ÀûÇÕµµ ÇÔ¼ö¸¦ ÀÌ¿ëÇÑ ÃÖÀûÀÇ ÃßõÀÚ ±×·ì »ý¼º ¹× À¯Áö ¾Ë°í¸®Áò
¿µ¹®Á¦¸ñ(English Title) Globally Optimal Recommender Group Formation and Maintenance Algorithm using the Fitness Function
ÀúÀÚ(Author) ±è¿ë±¸   À̹ÎÈ£   ¹Ú¼öÈ«   ȲöÁÖ   Yongku Kim   Minho Lee   Soohong Park   Cheolju Hwang  
¿ø¹®¼ö·Ïó(Citation) VOL 36 NO. 01 PP. 0050 ~ 0056 (2009. 02)
Çѱ۳»¿ë
(Korean Abstract)
º» ³í¹®¿¡¼­´Â P2P ³×Æ®¿öÅ© ȯ°æ¿¡¼­ À¯»çÇÑ Æ¯¼ºÀ» °¡Áø ´Ù¸¥ ³ëµå(node)¸¦ ã¾Æ ÃßõÀÚ(recommender) ±×·ìÀ» Çü¼ºÇÏ°í À¯ÁöÇÏ´Â »õ·Î¿î ¾Ë°í¸®ÁòÀ» Á¦¾ÈÇÑ´Ù. µÎ ³ëµåÀÇ À¯»çÇÑ Æ¯¼ºÀ» ºñ±³Çϱâ À§ÇØ º» ³í¹®¿¡¼­´Â µÎ ³ëµåÀÇ Æ¯¼º°ª(characteristic value. ÀÌÇÏ CV)À» ÀÌ¿ëÇÑ ÀûÇÕµµ °Ë»ç(fitness evaluation)¸¦ »ç¿ëÇÏ¿© À¯»çµµ(similarity)¸¦ È®ÀÎÇÑ´Ù. À¯»çµµÀÇ Å©±â°¡ ÀÛÀ»¼ö·Ï µÎ ³ëµå´Â ¸Å¿ì À¯»çÇÑ Æ¯¼ºÀ» °¡Áö°Ô µÈ´Ù. ¶ÇÇÑ, º» ³í¹®¿¡¼­ Á¦¾ÈÇÏ´Â GORGFM(Globally Optimal Recommender Group Formation and Maintenance) ¾Ë°í¸®ÁòÀº ÃÖ´Ü ±â°£ ³»¿¡ ÃÖÀûÀÇ ÃßõÀÚ ±×·ìÀ» Çü¼ºÇÏ°í »ç¿ëÀÚÀÇ ¼±È£µµ º¯È­¿¡ ´ëÀÀÇÒ ¼ö ÀÖ´Â ¾Ë°í¸®ÁòÀÌ´Ù. GORGFM ¾Ë°í¸®ÁòÀ» Æò°¡Çϱâ À§ÇØ º» ³í¹®¿¡¼­´Â ¸ÅĪÀ²(matching rate)°ú ¾ó¸¶³ª ºü¸£°í Á¤È®ÇÏ°Ô ÃßõÀÚ ±×·ìÀ» Çü¼ºÇϴ°¡¿¡ ´ëÇØ ½Ã¹Ä·¹ÀÌ¼Ç ÇÑ´Ù. GORGFM ¾Ë°í¸®ÁòÀº ³×Æ®¿öÅ©¿¡¼­»Ó¸¸ ¾Æ´Ï¶ó ÀÎÅͳݻ󿡼­ ÄÁÅÙÃ÷(contents) °Ë»ö µî°ú °°ÀÌ ÀûÇÕµµ ÇÔ¼ö(fitness function)¸¦ ÀÌ¿ëÇÒ ¼ö ÀÖ´Â ¸ðµç ½Ã½ºÅÛ¿¡ Àû¿ëÇÒ ¼ö ÀÖ´Ù.
¿µ¹®³»¿ë
(English Abstract)
This paper proposes a new algorithm of clustering similar nodes defined as nodes having similar characteristic values in pure P2P environment. To compare similarity between nodes, we introduce a fitness function whose return value depends only on the two nodes' characteristic values. The higher the return value is, the more similar the two nodes are. We propose a GORGFM algorithm newly in conjunction with the fitness function to recommend and exchange nodes¡¯ characteristic values for an interest group formation and maintenance. With the GORGFM algorithm, the interest groups are formed dynamically based on the similarity of users, and all nodes will highly satisfy with the information recommended and received from nodes of the interest group. To evaluate of performance of the GORGFM algorithm, we simulated a matching rate by the total number of nodes of network and the number of iterations of the algorithm to find similar nodes accurately. The result shows that the matching rate is highly accurate. The GORGFM algorithm proposed in this paper is highly flexible to be applied for any searching system on the web.
Å°¿öµå(Keyword) P2P ³×Æ®¿öÅ©   P2P Network   Ư¼º°ª   characteristic value   ÀûÇÕµµ °Ë»ç   fitness evaluation   À¯»çµµ   similarity   ÃßõÀÚ ±×·ì   GORGFM ¾Ë°í¸®Áò   recommender group   GORGFM algorithm  
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