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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö > Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö ÄÄÇ»ÅÍ ¹× Åë½Å½Ã½ºÅÛ

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Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) »óȲ±â¹Ý°ú Çù¾÷ ÇÊÅ͸µ ±â¹ýÀ» ÀÌ¿ëÇÑ °³ÀÎÈ­ ¿µÈ­ Ãßõ ½Ã½ºÅÛ
¿µ¹®Á¦¸ñ(English Title) Personalized Movie Recommendation System Using Context-Aware Collaborative Filtering Technique
ÀúÀÚ(Author) Min Jeong Kim   Doo-Soon Park   Min Hong   HwaMin Lee   ±è¹ÎÁ¤   ¹ÚµÎ¼ø   È«¹Î   ÀÌÈ­¹Î  
¿ø¹®¼ö·Ïó(Citation) VOL 04 NO. 09 PP. 0289 ~ 0296 (2015. 09)
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(Korean Abstract)
Á¤º¸ÀÇ Æø¹ßÀûÀÎ Áõ°¡·Î »ç¿ëÀÚµéÀº ¿øÇÏ´Â Á¤º¸¸¦ ºü¸¥ ½Ã°£¿¡ ¾ò´Â °ÍÀÌ Èûµé¾îÁ³´Ù. µû¶ó¼­ ÀÌ ¹®Á¦¸¦ ÇØ°áÇϱâ À§ÇÑ ´Ù¾çÇÑ ¹æ½ÄÀÇ »õ·Î¿î ¼­ºñ½ºµéÀÌ Á¦°øµÇ°í ÀÖ´Ù. °³Àο¡°Ô ¸Â´Â ¸ÂÃã ¼­ºñ½º¸¦ Á¦°øÇÏ´Â °ÍÀÌ Áß¿äÇÏ°Ô ºÎ°¢µÇ¸é¼­ °³ÀÎÈ­ Ãßõ ½Ã½ºÅÛÀÌ ¸Å¿ì Áß¿äÇÏ°Ô µÇ¾ú´Ù. Ãßõ ½Ã½ºÅÛ Áß Çù¾÷ ÇÊÅ͸µÀº Ãßõ ½Ã½ºÅÛ¿¡¼­ ³Î¸® »ç¿ëµÇ°í ÀÖ°í °³ÀÎÈ­ Ãßõ ½Ã½ºÅÛ Áß¿¡¼­ °¡Àå ¼º°øÀûÀÎ ¹æ¹ýÀÌ´Ù. Çù¾÷ ÇÊÅ͸µ ¹æ¹ýÀº °í°´µéÀÇ ÇÁ·ÎÆÄÀÏ Á¤º¸¸¦ ±â¹ÝÀ¸·Î ÃßõÀ» ÇϹǷΠÈñ¹Ú¼º ¹®Á¦¿Í cold-start ¹®Á¦°¡ ÀÖ´Ù. º» ³í¹®¿¡¼­´Â °³Àο¡°Ô ´õ Á¤È®ÇÏ°Ô ÃßõÇϱâ À§ÇØ Çù¾÷ ÇÊÅ͸µ ±â¹ý°ú »óȲ±â¹Ý ±â¹ýÀ» ÇÔ²² ÀÌ¿ëÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. »óȲ±â¹Ý ±â¹ýÀº »ç¿ëÀÚ¸¦ µÑ·¯½Ñ ½Ã°£, °¨Á¤, Àå¼Ò µî°ú °°Àº ȯ°æÀ» °í·ÁÇÏ¿© »ç¿ëÀÚ¿¡°Ô ¸Â´Â ¾ÆÀÌÅÛÀ» ÃßõÇÏ´Â ¹æ¹ýÀ¸·Î »óȲ¿¡ µû¶ó ´Þ¶óÁö´Â »ç¿ëÀÚÀÇ ¼±È£µµ¸¦ ¹Ý¿µÇÒ ¼ö ÀÖ´Ù. º» ³í¹®¿¡¼­´Â »óȲ±â¹Ý ±â¹ýÀ» È°¿ëÇϱâ À§ÇØ »óȲÁ¤º¸·Î °¨Á¤À» ÀÌ¿ëÇϸç À̸¦ À§ÇØ °³ÀÎÀÇ ÁÖ°üÀûÀÎ Á¤º¸¸¦ ÆľÇÇÏ´Â µ¥ È¿°úÀûÀÎ ¿µÈ­ ¸®ºä¸¦ ÀÌ¿ëÇÑ´Ù. º» ³í¹®¿¡¼­ Á¦¾ÈÇÑ ¹æ¹ýÀº ±âÁ¸ÀÇ Çù¾÷ ÇÊÅ͸µ ¹æ¹ýº¸´Ù ¼º´ÉÆò°¡ °á°ú, Çâ»óµÈ ¼º´ÉÀ» º¸¿´´Ù.
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(English Abstract)
The explosive growth of information has been difficult for users to get an appropriate information in time. The various ways of new services to solve problems has been provided. As customized service is being magnified, the personalized recommendation system has been important issue. Collaborative filtering system in the recommendation system is widely used, and it is the most successful process in the recommendation system. As the recommendation is based on customers¡¯profile, there can be sparsity and cold-start problems. In this paper, we propose personalized movie recommendation system using collaborative filtering techniques and context-based techniques. The context-based technique is the recommendation method that considers user¡¯s environment in term of time, emotion and location, and it can reflect user¡¯s preferences depending on the various environments. In order to utilize the context-based technique, this paper uses the human emotion, and uses movie reviews which are effective way to identify subjective individual information. In this paper, this proposed method shows outperforming existing collaborative filtering methods.
Å°¿öµå(Keyword) »óȲ±â¹Ý ±â¹ý   Çù¾÷ ÇÊÅ͸µ   ¿µÈ­ Ãßõ ½Ã½ºÅÛ   ¿µÈ­ ¸®ºä   Context-based Technique   Collaborative Filtering   Movie Recommendation   Movie Review  
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