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A correlative denoising autoencoder to model social influence for top-N recommender system

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Abstract

In recent years, there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation performance. In most cases, it requires a larger number of data to train a robust deep learning model, which contains a lot of parameters to fit training data. However, both data of user ratings and social networks are facing critical sparse problem, which makes it not easy to train a robust deep neural network model. Towards this problem, we propose a novel correlative denoising autoencoder (CoDAE) method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for recommendation. We develop the CoDAE model by utilizing three separated autoencoders to learn user features with roles of rater, truster and trustee, respectively. Especially, on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user, we propose to utilize shared parameters to learn common information of the units that corresponding to same users. Moreover, we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE model. We further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation task. The experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.

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Acknowledgements

This research has been supported by the National Natural Science Foundation of China (Grant No. 61472289) and the National Key Research and Development Project (2016YFC0106305).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Yiteng Pan, Fazhi He & Haiping Yu

Authors
  1. Yiteng Pan
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  2. Fazhi He
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  3. Haiping Yu
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Corresponding author

Correspondence to Fazhi He.

Additional information

Yiteng Pan received the MS degree from the School of Electronic Information, WuHan University, China in 2012. He is currently pursuing the PhD degree with the School of Computer Science in Wuhan University, China. His research interests include data mining, recommender systems, and deep learning.

Fazhi He received PhD degree from Wuhan University of Technology, China. He was post-doctor researcher in The State Key Laboratory of CAD&CG at Zhejiang University, China, a visiting researcher in Korea Advanced Institute of Science & Technology and a visiting faculty member in the University of North Carolina at Chapel Hill. Now he is a professor in School of Computer, Wuhan University, China. His research interests are computer graphics, computer-aided design, image processing, and computer supported cooperative work.

Haiping Yu is currently a PhD candidate at the school of computer science in Wuhan University, China. Her research interests are pattern recognition, image processing, and computer graphics.

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Pan, Y., He, F. & Yu, H. A correlative denoising autoencoder to model social influence for top-N recommender system. Front. Comput. Sci. 14, 143301 (2020). https://doi.org/10.1007/s11704-019-8123-3

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