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Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
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´ÙÀ½°Ç
ÇѱÛÁ¦¸ñ(Korean Title)
±×·ì Ãßõ¿¡¼ »ç¿ëÀÚ ¼±È£µµÀÇ ÆíÂ÷¸¦ °í·ÁÇÑ ±×·ì ¸ðµ¨¸µ Àü·«
¿µ¹®Á¦¸ñ(English Title)
A Group Modeling Strategy Considering Deviation of the User¡¯s Preference in Group Recommendation
ÀúÀÚ(Author)
±èÇüÁø
¼¿µ´ö
¹éµÎ±Ç
HyungJin Kim
Young-Duk Seo
Doo-Kwon Baik
¿ø¹®¼ö·Ïó(Citation)
VOL 43 NO. 10 PP. 1144 ~ 1153 (2016. 10)
Çѱ۳»¿ë
(Korean Abstract)
±×·ì ÃßõÀº °³ÀÎÀÌ ¾Æ´Ñ ±×·ìÀÇ Æ¯¼º ¹× ¼ºÇâÀ» ºÐ¼®ÇÏ¿© ±¸¼º¿øµé¿¡°Ô ÀûÇÕÇÑ Á¤º¸¸¦ Á¦°øÇÏ´Â Ãßõ ¹æ½ÄÀÌ´Ù. ±âÁ¸ÀÇ ±×·ì Ãßõ ¹æ½ÄÀº Æò±Õ ¼±È£µµ³ª ¼±È£ Ƚ¼ö¿¡ ±â¹ÝÇÑ ±×·ì ¸ðµ¨¸µ Àü·«À» »ç¿ëÇÑ´Ù. ÇÏÁö¸¸ Æò±ÕÀÌ ³ô°í ¼±È£ Ƚ¼ö°¡ ¸¹Àº °ü½É»ç´õ¶óµµ ¼±È£µµÀÇ ÆíÂ÷°¡ Å©´Ù¸é, ±×·ì ³» ±¸¼º¿ø ¸ðµÎ¸¦ ¸¸Á·½ÃÅ°´Â Ãßõ °á°ú¸¦ Á¦°øÇϱⰡ ¾î·Æ´Ù. º» ³í¹®¿¡¼´Â À̸¦ °³¼±ÇÏ°íÀÚ °ü½É»ç¿¡ ´ëÇÑ Æò±Õ ¼±È£µµ¿¡ ¼±È£µµ ÆíÂ÷¸¦ °¡ÁßÄ¡·Î ÇÏ´Â ±×·ì ¸ðµ¨¸µ Àü·«À» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÏ´Â ¹æ¹ýÀº Æò±Õ ¼±È£µµ°¡ ³ôÀ¸¸é¼ ¼±È£µµ ÆíÂ÷°¡ ÀÛÀº °ü½É»çµéÀ» Ãßõ °á°ú·Î Á¦°øÇØÁÜÀ¸·Î½á ±âÁ¸ÀÇ ±×·ì ¸ðµ¨¸µ Àü·«º¸´Ù ´õ ¸¹Àº ±×·ì ³» ±¸¼º¿øµéÀ» ¸¸Á·½ÃÅ°´Â Á¤º¸¸¦ Á¦°øÇÏ´Â °ÍÀÌ °¡´ÉÇÏ´Ù. ½ÇÇèÀ» ÅëÇØ Á¦¾ÈÇÏ´Â ±×·ì ¸ðµ¨¸µ Àü·«ÀÌ ±âÁ¸ÀÇ ¹æ½Ä¿¡ ºñÇØ ³ôÀº ¼º´ÉÀ» º¸¿´°í, ¼Ò±Ô¸ðÀÇ »ç¿ëÀÚ»Ó¸¸ ¾Æ´Ï¶ó ¸¹Àº ¼öÀÇ »ç¿ëÀÚ°¡ Çü¼ºÇÏ´Â ±×·ì¿¡¼µµ ³ôÀº ¼º´ÉÀ» °¡ÁüÀ» È®ÀÎÇÏ¿´´Ù.
¿µ¹®³»¿ë
(English Abstract)
Group recommendation analyzes the characteristics and tendency of a group rather than an individual and provides relevant information for the members of the group. Existing group recommendation methods merely consider the average and frequency of a preference. However, if the users¡¯ preferences have large deviations, it is difficult to provide satisfactory results for all users in the group, although the average and frequency values are high. To solve these problems, we propose a method that considers not only the average of a preference but also the deviation. The proposed method provides recommendations with high average values and low deviations for the preference, so it reflects the tendency of all group members better than existing group recommendation methods. Through a comparative experiment, we prove that the proposed method has better performance than existing methods, and verify that it has high performance in groups with a large number of members as well as in small groups.
Å°¿öµå(Keyword)
recommender system
group recommendation
group modeling
aggregation strategy of the group modeling
deviation of the preference
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