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ÇѱÛÁ¦¸ñ(Korean Title) |
Çùµ¿Àû ÇÊÅ͸µ¿¡¼ °íÇ°Áú ¿¹ÃøÀ» À§ÇÑ È¿°úÀûÀÎ Ãßõ ¾Ë°í¸®Áò |
¿µ¹®Á¦¸ñ(English Title) |
Effective Recommendation Algorithms for Higher Quality Prediction in Collaborative Filtering |
ÀúÀÚ(Author) |
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Taekhun Kim
Seokin Park
Sungbong Yang
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¿ø¹®¼ö·Ïó(Citation) |
VOL 16 NO. 11 PP. 1116 ~ 1120 (2010. 11) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
In this paper we present two refined neighbor selection algorithms for recommender systems and also show how the attributes of the items can be used for higher prediction quality. The refined neighbor selection algorithms adopt the transitivity-based neighbor selection method using virtual neighbors and alternate neighbors, respectively. The experimental results show that the recommender systems with the proposed algorithms outperform other systems and they can overcome the large scale dataset problem as well as the first rater problem without deteriorating prediction quality.
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Å°¿öµå(Keyword) |
Ãßõ½Ã½ºÅÛ
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ÀÌ¿ô¼±Á¤¾Ë°í¸®Áò
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Recommender systems
Collaborative filtering
Neighbor selection algorith
Prediction quality
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