• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

Vol. 15, No. 7, July 31, 2021
10.3837/tiis.2021.07.006, Download Paper (Free):

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.


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Cite this article

[IEEE Style]
L. Duan, W. Wang and B. Han, "A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning," KSII Transactions on Internet and Information Systems, vol. 15, no. 7, pp. 2399-2413, 2021. DOI: 10.3837/tiis.2021.07.006.

[ACM Style]
Li Duan, Weiping Wang, and Baijing Han. 2021. A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning. KSII Transactions on Internet and Information Systems, 15, 7, (2021), 2399-2413. DOI: 10.3837/tiis.2021.07.006.

[BibTeX Style]
@article{tiis:24804, title="A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning", author="Li Duan and Weiping Wang and Baijing Han and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2021.07.006}, volume={15}, number={7}, year="2021", month={July}, pages={2399-2413}}