JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Effective Pre-rating Method Based on Users' Dichotomous Preferences and Average Ratings Fusion for Recommender Systems |
| 영문제목(English Title) |
Effective Pre-rating Method Based on Users' Dichotomous Preferences and Average Ratings Fusion for Recommender Systems |
| 저자(Author) |
Shulin Cheng
Wanyan Wang
Shan Yang
Xiufang Cheng
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| 원문수록처(Citation) |
VOL 17 NO. 03 PP. 0462 ~ 0472 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With an increase in the scale of recommender systems, users’ rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users’ dichotomous preferences and average ratings fusion. First, based on a user–item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated. |
| 키워드(Keyword) |
Collaborative Filtering
Data Sparsity
Fusion Filling
Preference Matrix
Recommender System
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| 파일첨부 |
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