Intelligent collaborative recommendation method based on spectral clustering and latent factor model

With the rapid development of cloud computing and mobile Internet technique, the amount of cloud offers and online information has been growing explosively, which yields information overload. To deal with the data sparsity problem and the cold start problem in recommender systems, an intelligent collaborative recommendation method based on spectral clustering and latent factor model was proposed. Similar users were clustered with the spectral clustering scheme according to the label features of users. The original rating matrix was transformed into multiple low-dimensional sub-matrix where the factorization-based latent factor model was employed to predict the missing data locally. Afterwards, the final predictions could be made globally based on the improved neighbor-based collaborative recommendation algorithm. The proposed method was effective in dealing with the data sparsity problem and the cold start problem. Experimental results validated that the proposed method was improved in recommendation accuracy and efficiency. © 2021, Editorial Department of CIMS. All right reserved.