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Open AccessArticle

User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering

School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
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Symmetry 2020, 12(1), 121; https://doi.org/10.3390/sym12010121 (registering DOI)
Received: 12 December 2019 / Revised: 26 December 2019 / Accepted: 6 January 2020 / Published: 7 January 2020
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. View Full-Text
Keywords: recommendation system; rating prediction; SVD++; user embedding recommendation system; rating prediction; SVD++; user embedding
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Shi, W.; Wang, L.; Qin, J. User Embedding for Rating Prediction in SVD++-Based Collaborative Filtering. Symmetry 2020, 12, 121.

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