JICCE (한국정보통신학회)
Current Result Document :
| 한글제목(Korean Title) |
Collaborative Filtering Algorithm Based on User–Item Attribute Preference |
| 영문제목(English Title) |
Collaborative Filtering Algorithm Based on User–Item Attribute Preference |
| 저자(Author) |
JiaQi Ji
Yeongjee Chung
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| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0135 ~ 0141 (2019. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Collaborative filtering algorithms often encounter data sparsity issues. To overcome this issue, auxiliary information of relevant items is analyzed and an item attribute matrix is derived. In this study, we combine the user–item attribute preference with the traditional similarity calculation method to develop an improved similarity calculation approach and use weights to control the importance of these two elements. A collaborative filtering algorithm based on user–item attribute preference is proposed. The experimental results show that the performance of the recommender system is the most optimal when the weight of traditional similarity is equal to that of user–item attribute preference similarity. Although the rating-matrix is sparse, better recommendation results can be obtained by adding a suitable proportion of user–item attribute preference similarity. Moreover, the mean absolute error of the proposed approach is less than that of two traditional collaborative filtering algorithms.
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| 키워드(Keyword) |
Collaborative filtering
Recommender system
Attribute preference
Data sparsity
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| 파일첨부 |
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