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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 :
9
/ 9
ÀÌÀü°Ç
ÇѱÛÁ¦¸ñ(Korean Title)
»ç¿ëÀÚ Á¤º¸¸¦ ÀÌ¿ëÇÑ ¸ð¹ÙÀÏ Ãßõ ±â¹ý
¿µ¹®Á¦¸ñ(English Title)
The User Information-based Mobile Recommendation Technique
ÀúÀÚ(Author)
À±¼Ò¿µ
À±¼º´ë
So-Young Yun
Sung-Dae Youn
¿ø¹®¼ö·Ïó(Citation)
VOL 18 NO. 02 PP. 0379 ~ 0386 (2014. 02)
Çѱ۳»¿ë
(Korean Abstract)
¸ð¹ÙÀÏ ±â±âÀÇ »ç¿ëÀÌ ±ÞÁõÇÏ¸é¼ ¾Û ½ºÅä¾î¸¦ ÀÌ¿ëÇÏ´Â »ç¿ëÀÚµé ¶ÇÇÑ Áõ°¡ÇÏ°í ÀÖ´Ù. ±×·¯³ª ¾Û ½ºÅä¾îµéÀº ´ëºÎºÐ ´Ü¼øÇÑ ·©Å· ¹æ½ÄÀÇ ÃßõÀ» »ç¿ëÇϹǷΠÃßõÀÇ Á¤È®¼ºÀÌ ¶³¾îÁø´Ù. º» ³í¹®¿¡¼´Â »ç¿ëÀÚ¿¡°Ô ´õ ÀûÇÕÇÑ ¾ÆÀÌÅÛÀ» ÃßõÇϱâ À§ÇØ »ç¿ëÀÚ Á¤º¸ °¡ÁßÄ¡¿Í ¾ÆÀÌÅÛÀÇ ÃÖ±Ù ¼±È£ Á¤µµ¸¦ ¹Ý¿µÇÑ ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÏ´Â ±â¹ýÀº µ¥ÀÌÅÍ ¼ÂÀ» Ä«Å×°í¸®º°·Î ±¸ºÐÇÑ ÈÄ Çù¾÷ÇÊÅ͸µ ±â¹ý¿¡ »ç¿ëÀÚ Á¤º¸ °¡ÁßÄ¡¸¦ Àû¿ëÇÏ¿© ¿¹Ãø°ªÀ» ÃßÃâÇÑ´Ù. Ä«Å×°í¸®º°·Î ¾ÆÀÌÅÛ¿¡ ´ëÇÑ ÃÖ±Ù ¼±È£ Á¤µµ¸¦ ¹Ý¿µÇϱâ À§ÇØ Æ¯Á¤ ±â°£À» ÁöÁ¤ÇÑ ¾ÆÀÌÅÛ Æò°¡°ª Æò±ÕÀ» ±¸ÇÑ´Ù. ÃÖÁ¾ÀûÀ¸·Î µÎ °á°ú °ªÀ» °áÇÕÇÏ¿© ¾ÆÀÌÅÛÀ» ÃßõÇÑ´Ù. ½ÇÇèÀ» ÅëÇØ Á¦¾ÈÇÑ ±â¹ýÀÌ ±âÁ¸ÀÇ ¾ÆÀÌÅÛ ±â¹Ý, »ç¿ëÀÚ ±â¹Ý ±â¹ýº¸´Ù ÃßõÀÇ Á¤È®¼º°ú ÀûÇÕ¼ºÀÌ Çâ»óµÇ´Â °ÍÀ» È®ÀÎÇÏ¿´´Ù.
¿µ¹®³»¿ë
(English Abstract)
As the use of mobile device is increasing rapidly, the number of users is also increasing. However, most of the app stores are using recommendation of simple ranking method, so the accuracy of recommendation is lower. To recommend an item that is more appropriate to the user, this paper proposes a technique that reflects the weight of user information and recent preference degree of item. The proposed technique classifies the data set by categories and then derives a predicted value by applying the user¡¯s information weight to the collaborative filtering technique. To reflect the recent preference degree of item by categories, the average of items¡¯ rating values in the designated period is computed. An item is recommended by combining the two result values. The experiment result indicated that the proposed method has been more enhanced the accuracy, appropriacy, compared to item-based, user-based method.
Å°¿öµå(Keyword)
Ãßõ±â¹ý
Çù¾÷ÇÊÅ͸µ
»ç¿ëÀÚÁ¤º¸
À¯»çµµ
°¡ÁßÄ¡
Recommender Technique
Collaborative Filtering
User Information
Similarity
Weight
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