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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Current Result Document : 8 / 9 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) »ç¿ëÀÚ Á¤º¸ °¡ÁßÄ¡¸¦ ÀÌ¿ëÇÑ Ãßõ ±â¹ý
¿µ¹®Á¦¸ñ(English Title) A Recommendation Technique using Weight of User Information
ÀúÀÚ(Author) À±¼Ò¿µ   À±¼º´ë   So Young Yun   Sung-Dae Youn  
¿ø¹®¼ö·Ïó(Citation) VOL 15 NO. 04 PP. 0877 ~ 0885 (2011. 04)
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(Korean Abstract)
Çù¾÷ ÇÊÅ͸µÀº Ãßõ½Ã½ºÅÛµé Áß¿¡¼­ °¡Àå ³Î¸® »ç¿ëµÇ´Â ±â¹ýÀÌ´Ù. ±×·¯³ª Çù¾÷ ÇÊÅ͸µÀº ÃßõÀÇ Á¤È®¼ºÀ» ¶³¾î¶ß¸®´Â Èñ¼Ò¼º°ú È®À强ÀÇ ¹®Á¦¸¦ °¡Áö°í ÀÖÀ¸¸ç À̸¦ ÇØ°áÇϱâ À§ÇÑ ´Ù¾çÇÑ ¿¬±¸°¡ ÀÌ·ç¾îÁö°í ÀÖ´Ù. º» ³í¹®¿¡¼­´Â Çù¾÷ÇÊÅ͸µÀÇ Èñ¼Ò¼º°ú È®À强ÀÇ ¹®Á¦¸¦ ÇØ°áÇϱâ À§ÇØ °¡ÁßÄ¡¸¦ »ç¿ëÇÑ ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÑ ±â¹ýÀº µ¥ÀÌÅÍ ¼Â¿¡¼­ ÃßõÀÇ Á¤È®¼ºÀ» ³ôÀ̱â À§ÇØ Æò°¡°ªÀÌ 4ÀÌ»óÀÎ µ¥ÀÌÅ͵鸸À» »ç¿ëÇÏ¿© ¾ÆÀÌÅÛÀ» ¼±È£ÇÏ´Â »ç¿ëÀÚ Á¤º¸¸¦ ºÐ¼®ÇÑ´Ù. ¾ÆÀÌÅÛÀÇ À帣 Á¤º¸¿Í ºÐ¼®ÇÑ »ç¿ëÀÚ Á¤º¸¸¦ À¯»çµµ °è»ê ½Ã °¡ÁßÄ¡·Î »ç¿ëÇÏ°í ÀÓ°è°ª ÀÌ»óÀÇ À¯»çµµ¸¦ °¡Áø µ¥ÀÌÅ͵鸸À¸·Î ¿¹Ãø°ªÀ» °è»êÇÏ¿© Æò°¡µÇÁö ¾ÊÀº µ¥ÀÌÅÍÀÇ Æò°¡°ªÀ¸·Î »ç¿ëÇÑ´Ù. Á¦¾ÈÇÑ ±â¹ýÀº ¾ÆÀÌÅÛ¿¡ ´ëÇÑ Æ¯¼ºÀ» ºÐ¼®ÇÏ¿© ¿¹Ãø°ªÀ» °è»êÇÔÀ¸·Î½á Èñ¼Ò¼ºÀ» ÁÙÀÓ°ú µ¿½Ã¿¡ Á¤È®¼ºÀ» ´õ ³ôÀÏ ¼ö ÀÖ°í »õ·Î¿î ¾ÆÀÌÅÛ°ú »ç¿ëÀÚ°¡ µî·ÏµÇ¾úÀ» ¶§ ºÐ¼®µÈ Á¤º¸¸¦ ¹ÙÅÁÀ¸·Î ºü¸¥ ºÐ·ù°¡ °¡´ÉÇÏ´Ù. ½ÇÇèÀ» ÅëÇØ Á¦¾ÈÇÑ ±â¹ýÀÌ ±âÁ¸ÀÇ ¾ÆÀÌÅÛ ±â¹Ý, À帣 ±â¹Ý ±â¹ýº¸´Ù ÃßõÀÇ Á¤È®¼ºÀÌ Çâ»óµÇ´Â °ÍÀ» È®ÀÎÇÏ¿´´Ù.
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(English Abstract)
A collaborative filtering(CF) is the most widely used technique in recommender system. However, CF has sparsity and scalability problems. These problems reduce the accuracy of recommendation and extensive studies have been made to solve these problems, In this paper, we proposed a method that uses a weight so as to solve these problems. After creating a user-item matrix, the proposed method analyzes information about users who prefer the item only by using data with a rating over 4 for enhancing the accuracy in the recommendation. The proposed method uses information about the genre of the item as well as analyzed user information as a weight during the calculation of similarity, and it calculates prediction by using only data for which the similarity is over a threshold and uses the data as the rating value of unrated data. It is possible simultaneously to reduce sparsity and to improve accuracy by calculating prediction through an analysis of the characteristics of an item. Also, it is possible to conduct a quick classification based on the analyzed information once a new item and a user are registered. The experiment result indicated that the proposed method has been more enhanced the accuracy, compared to item based, genre based methods.
Å°¿öµå(Keyword) Çù¾÷ ÇÊÅ͸µ   Ãßõ ±â¹ý   Èñ¼Ò¼º   È®À强   À¯»çµµ   °¡ÁßÄ¡   Collaborative Filtering   Recommendation Technique   Sparsity   Scalability   Similarity   Weight  
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