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Trust-Aware Collaborative Filtering with a Denoising Autoencoder

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Abstract

Collaborative filtering is one of the most successful and extensive methods used by recommender systems for predicting the preferences of users. However, traditional collaborative filtering only uses rating information to model the user, the data sparsity problem and the cold start problem will severely reduce the recommendation performance. To overcome these problems, we propose two neural network models to improve recommendations. The first one called TDAE uses a denoising autoencoder to integrate the ratings and the explicit trust relationships between users in the social networks in order to model the preferences of users more accurately. However, the explicit trust information is very sparse, which limits the performance of this model. Therefore, we propose a second method called TDAE++ for extracting the implicit trust relationships between users with similarity measures, where we employ both the explicit and implicit trust information together to improve the quality of recommendations. Finally, we inject the trust information into both the input and the hidden layer in order to fuse these two types of different information to learn more reliable semantic representations of users. Comprehensive experiments based on three popular data sets verify that our proposed models perform better than other state-of-the-art approaches in common recommendation tasks.

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References

  1. Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40:56–58

    Article  Google Scholar 

  2. Schafer JB, Konstan J, Riedl J (2001) E-commerce recommendation applications. IEEE Internet Comput 5:115–153

    MATH  Google Scholar 

  3. Herlocker JL, Konstan JA, Terveen LG, Triedl JT (2004) Collaborative filtering recommender systems. ACM Trans Inf Syst 4:5–53

    Article  Google Scholar 

  4. Adams RP, Dahl GE, Murray I (2010) Incorporating side information in probabilistic matrix factorization with Gaussian processes. Papeles De Poblacin, pp 33–57

  5. Porteous I, Asuncion AU, Welling M (2010) Bayesian matrix factorization with side information and Dirichlet process mixtures. M. Fox and D. Poole, AAAI, AAAI Press, New York

    Google Scholar 

  6. Scott J (1988) Social network analysis. Sociology 22:109–127

    Article  Google Scholar 

  7. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM conference on recommender systems, pp 135–142

  8. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 426–434

  9. Guo G, Zhang J, Yorke-Smith N (2015) Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: 29th AAAI conference on artificial intelligence. AAAI Press, pp 123–129

  10. Hong C, Yu J, Tao D, Wang M (2014) Image-based 3D human pose recovery by multi-view locality sensitive sparse retrieval. IEEE Trans Ind Electron 62(2):3742–3751

    Google Scholar 

  11. Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24:5659–5670

    Article  MathSciNet  MATH  Google Scholar 

  12. Liu W, Ma T, Xie Q, Tao D, Cheng J (2017) LMAE: a large margin auto-encoders for classification. Signal Process 141:137–143

    Article  Google Scholar 

  13. Liu W, Ma T, Tao D, You J (2016) HSAE: a Hessian regularized sparse auto-encoders. Neurocomputing 187:59–65

    Article  Google Scholar 

  14. Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-N recommender systems. In: ACM international conference on web search and data mining. ACM, pp 153–162

  15. Pan Y, He F, Yu H (2017) Trust-aware top-N recommender systems with correlative denoising autoencoder. arXiv:1703.01760

  16. Deng S, Huang L, Xu G, Wu X, Wu Z (2017) On deep learning for trust-aware recommendations in social networks. IEEE Trans Neural Netw Learn Syst 28(5):1164

    Article  Google Scholar 

  17. Scholkopf B, Platt J, Hofmann T (2006) Greedy layer-wise training of deep networks. In: International conference on neural information processing systems. MIT Press, pp 153–160

  18. Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning. ACM, pp 1096–1103

  19. Adomavicius G, Tuzhilin A (2005) Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17:734–749

    Article  Google Scholar 

  20. Teytaud O, Gelly S, Mary J (2007) Active learning in regression, with application to stochastic dynamic programming. In: International conference on informatics in control, automation and robotics, ICINCO and CAP, pp 373–386

  21. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  22. Strub F, Mary J (2015) Collaborative filtering with stacked denoising autoencoders and sparse inputs. In: NIPS workshop on machine learning for e-commerce

  23. Dziugaite GK, Roy DM (2015) Neural network matrix factorization. arXiv:1511.06443

  24. Base LT (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  25. Strub F, Gaudel R, Mary J (2016) Hybrid recommender system based on autoencoders. In: The workshop on deep learning for recommender systems. ACM, pp 11–16

  26. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on machine learning. ACM, pp 791–798

  27. Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on world wide web companion, pp 111–112

  28. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning, pp 689–696

  29. Zheng Y, Tang B, Ding W, Zhou H (2016) Neural autoregressive collaborative filtering for implicit feedback. In: Proceedings of the 1st workshop on deep learning for recommender systems

  30. Zheng Y, Tang B, Ding W, Zhou H (2016) A neural autoregressive approach to collaborative filtering. In: International conference on machine learning (ICML), pp 764–773

  31. Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: 8th IEEE international conference on data mining. IEEE, pp 263–272

  32. Papagelis M, Plexousakis D, Kutsuras T (2005) Alleviating the sparsity problem of collaborative filtering using trust inferences. In: 3rd international conference, iTrust 2005, proceedings DBLP, pp 224–239

  33. Wang J, Hu J, Qiao S, Sun W, Zang X, Zhang B (2016) Recommendation with implicit trust relationship based on users similarity. In: International conference on manufacturing science and information engineering (ICMSIE), pp 373–378

  34. Mnih A, Salakhutdinov R (2008) Probabilistic matrix factorization. In: Neural information processing systems, pp 1257–1264

  35. Yang B, Lei Y, Liu D, Liu J (2013) Social collaborative filtering by trust. In: Proceedings of the 23rd international joint conference on artificial intelligence, pp 2747–2753

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (71473035, 11501095), Jilin Provincial Science and Technology Department of China (20150204040GX, 20170520051Jh), and Jilin Province Development and Reform Commission Projects (2015Y055, 2015Y054).

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Authors and Affiliations

  1. School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China

    Meiqi Wang, Zhiyuan Wu, Xiaoxin Sun, Guozhong Feng & Bangzuo Zhang

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  1. Meiqi Wang
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  2. Zhiyuan Wu
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  3. Xiaoxin Sun
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  4. Guozhong Feng
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  5. Bangzuo Zhang
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Correspondence to Bangzuo Zhang.

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Wang, M., Wu, Z., Sun, X. et al. Trust-Aware Collaborative Filtering with a Denoising Autoencoder. Neural Process Lett 49, 835–849 (2019). https://doi.org/10.1007/s11063-018-9831-7

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