Abstract
The collaborative filtering algorithm based on the singular value decomposition plus plus (SVD++) model employs the linear interactions between the latent features of users and items to predict the rating in the recommendation systems. Aiming to further enrich the user model with explicit feedback, this paper proposes a user embedding model for rating prediction in SVD++-based collaborative filtering, named UE-SVD++. We exploit the user potential explicit feedback from the rating data and construct the user embedding matrix by the proposed user-wise mutual information values. In addition, the user embedding matrix is added to the existing user bias and implicit parameters in the SVD++ to increase the accuracy of the user modeling. Through extensive studies on four different datasets, we found that the rating prediction performance of the UE-SVD++ model is improved compared with other models, and the proposed model’s evaluation indicators root-mean-square error (RMSE) and mean absolute error (MAE) are decreased by 1.002–2.110% and 1.182–1.742%, respectively.
Highlights
The past decades witnessed strong growth of internet use worldwide
We propose a novel rating prediction model based on user embedding and SVD++
The user embedding matrix is added to the existing user bias and implicit parameters in the SVD++ to model the user features more accurately on rating prediction
Summary
The past decades witnessed strong growth of internet use worldwide. The amount of information on the internet is increasing explosively, giving rise to the problem of “information overload”. Recommendation systems recommended items in which a particular user may be interested from user–item interaction information such as ratings Under these circumstances, the accuracy of the rating prediction directly affects the recommended effect. The rating data have the characteristics of high sparsity and uneven distribution, leading to several problems such as low recommendation performance To solve this problem, researchers introduced additional information about the user or item into the MF model, and they obtained good recommendation results to some extent. We propose a novel rating prediction model based on user embedding and SVD++. The user embedding matrix is added to the existing user bias and implicit parameters in the SVD++ to model the user features more accurately on rating prediction.
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