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| 한글제목(Korean Title) |
A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning |
| 영문제목(English Title) |
A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning |
| 저자(Author) |
Li Duan
Weiping Wang
Baijing Han
|
| 원문수록처(Citation) |
VOL 15 NO. 07 PP. 2399 ~ 2413 (2021. 07) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset. |
| 키워드(Keyword) |
Recommendation
Collaborative Filtering
clustering
Supervised Learning
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| 파일첨부 |
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