Open AccessRating-Based Collaborative Filtering Using Spectral Clustering Algorithm
Author(s) -
Yongjie Yan,
Hui Xie,
Ma Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/3/032022
Subject(s) - cluster analysis , computer science , cure data clustering algorithm , correlation clustering , canopy clustering algorithm , spectral clustering , data mining , data stream clustering , collaborative filtering , algorithm , artificial intelligence , pattern recognition (psychology) , machine learning , recommender system
Clustering analysis has been an important area of machine learning and data mining research, it can help us know the connection between things more clearly. In recent years, the research of spectral clustering algorithm has been a new and efficient clustering analysis algorithm. In this paper, the sparsity and the real-time problem of traditional recommendation algorithms, a new recommendation algorithm based on spectral clustering is proposed. The spectral clustering process can improve the efficiency of spectral clustering algorithm. Spectral clustering can be performed offline, which will accelerate the speed of online recommendation. The experimental results on Movie lens show that the new algorithm improves recommendation quality in MAE and coverage.