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http://dx.doi.org/10.3837/tiis.2021.07.006

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

Duan, Li (Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University)
Wang, Weiping (School of Computer and Communication Engineering, University of Science and Technology Beijing)
Han, Baijing (School of Computer and Communication Engineering, University of Science and Technology Beijing)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.7, 2021 , pp. 2399-2413 More about this Journal
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.
Keywords
Recommendation; Collaborative filtering; Clustering; Supervised learning;
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