Please wait a minute...
Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2020, Vol. 14 Issue (2) : 430-450    https://doi.org/10.1007/s11704-018-8052-6
REVIEW ARTICLE
A survey of autoencoder-based recommender systems
Guijuan ZHANG, Yang LIU, Xiaoning JIN()
Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China
 Download: PDF(642 KB)  
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

In the past decade, recommender systems have been widely used to provide users with personalized products and services. However, most traditional recommender systems are still facing a challenge in dealing with the huge volume, complexity, and dynamics of information. To tackle this challenge, many studies have been conducted to improve recommender system by integrating deep learning techniques. As an unsupervised deep learning method, autoencoder has been widely used for its excellent performance in data dimensionality reduction, feature extraction, and data reconstruction. Meanwhile, recent researches have shown the high efficiency of autoencoder in information retrieval and recommendation tasks. Applying autoencoder on recommender systems would improve the quality of recommendations due to its better understanding of users’ demands and characteristics of items. This paper reviews the recent researches on autoencoder-based recommender systems. The differences between autoencoder-based recommender systems and traditional recommender systems are presented in this paper. At last, some potential research directions of autoencoder-based recommender systems are discussed.

Keywords recommender system      autoencoder      deep learning      data mining     
Corresponding Author(s): Xiaoning JIN   
Just Accepted Date: 20 November 2018   Online First Date: 17 September 2019    Issue Date: 16 October 2019
 Cite this article:   
Guijuan ZHANG,Yang LIU,Xiaoning JIN. A survey of autoencoder-based recommender systems[J]. Front. Comput. Sci., 2020, 14(2): 430-450.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-8052-6
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I2/430
1 L Soghra, Ebrahimpour-komleh H. Improving collaborative recommender systems via emotional features. In: Proceedings of the 10th IEEE International Conference on Application of Information and Communication Technologies (AICT). 2016, 1–5
2 S Zhang, L Yao, A Sun, Y Tay. Deep learning based recommender system: a survey and new perspectives. 2017, arXiv preprint arXiv:1707.07435
3 F Cacheda, V Carneiro, D Fernández, V Formoso. Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web (TWEB), 2011, 5(1): 1–33
https://doi.org/10.1145/1921591.1921593
4 A T Nguyen, N Denos, C Berrut. Improving new user recommendations with rule-based induction on cold user data. In: Proceedings of the 2007 ACM Conference on Recommender Systems. 2007, 121–128
https://doi.org/10.1145/1297231.1297251
5 A M Rashid, I Albert, D Cosley, S K Lam, S W McNee, J A Konstan, J Riedl. Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th ACM International Conference on Intelligent User Interfaces. 2002, 127–134
https://doi.org/10.1145/502721.502737
6 T Ebesu, Y Fang. Neural semantic personalized ranking for item coldstart recommendation. Information Retrieval Journal, 2017, 20(2): 109–131
https://doi.org/10.1007/s10791-017-9295-9
7 R Chow, H Jin, B Knijnenburg, G Saldamli. Differential data analysis for recommender systems. In: Proceedings of the 7th ACM Conference on Recommender Systems. 2013, 323–326
https://doi.org/10.1145/2507157.2507190
8 C A Gomez-Uribe, N Hunt. The netflix recommender system: algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 2015, 6(4): 1–19
https://doi.org/10.1145/2843948
9 G Sottocornola, F Stella, M Zanker, F Canonaco. Towards a deep learning model for hybrid recommendation. In: Proceedings of the International Conference on Web Intelligence. 2017, 1260–1264
https://doi.org/10.1145/3106426.3110321
10 S Yan, K J Lin, X Zheng, W Zhang, X Feng. An approach for building efficient and accurate social recommender systems using individual relationship networks. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10): 2086–2099
https://doi.org/10.1109/TKDE.2017.2717984
11 H Wu, Z Zhang, K Yue, B Zhang, R Zhu. Content embedding regularized matrix factorization for recommender systems. In: Proceedings of the 2017 IEEE International Congress on Big Data (BigData Congress). 2017, 209–215
https://doi.org/10.1109/BigDataCongress.2017.36
12 J McAuley, J Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems. 2013, 165–172
https://doi.org/10.1145/2507157.2507163
13 I Goodfellow, Y Bengio, A Courville. Deep Learning. Cambridge. MA: MIT Press, 2016
14 X Peng, Y Li, X Wei, J Luo, Y L Marphey. Traffic sign recognition with transfer learning. In: Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI). 2017, 1–7
https://doi.org/10.1109/SSCI.2017.8285332
15 A Dehghan, E G Ortiz, R Villegas, M Shah. Who do I look like? Determining parent-offspring resemblance via gated autoencoders. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1757–1764
https://doi.org/10.1109/CVPR.2014.227
16 X Lu, T Yu, S Matsuda, C Hori. Speech enhancement based on deep denoising autoencoder. Interspeech, 2013, 436–440
17 X Li, J She. Collaborative variational autoencoder for recommender systems. In: Proceedings of the 23rd ACM International Conference on Knowledge Discovery and Data Mining. 2017, 305–314
https://doi.org/10.1145/3097983.3098077
18 F Zhang, N J Yuan, D Lian, X Xie, W Y Ma. Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM International Conference on Knowledge Discovery and Data Mining. 2016, 353–362
https://doi.org/10.1145/2939672.2939673
19 M Unger. Latent context-aware recommender systems. In: Proceedings of the 9th ACM Conference on Recommender Systems. 2015, 383–386
20 M Unger, A Bar, B Shapira, L Rokach. Towards latent contextaware recommendation systems. Knowledge-Based Systems, 2016, 104: 165–178
https://doi.org/10.1016/j.knosys.2016.04.020
21 X Li, J She. Relational variational autoencoder for link prediction with multimedia data. In: Proceedings of the Thematic Workshops of ACM Multimedia. 2017, 93–100
https://doi.org/10.1145/3126686.3126774
22 S Li, J Kawale, Y Fu. Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015, 811–820
https://doi.org/10.1145/2806416.2806527
23 D Rafailidis, F Crestani. Recommendation with social relationships via deep learning. In: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval. 2017, 151–158
https://doi.org/10.1145/3121050.3121057
24 G Adomavicius, A Tuzhilin. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734–749
https://doi.org/10.1109/TKDE.2005.99
25 J Lu, Y Guo, Z Mi, Y Yang. Trust-enhanced matrix factorization using pagerank for recommender system. In: Proceedings of the International Conference on Computer, Information and Telecommunication Systems (CITS). 2017, 123–127
https://doi.org/10.1109/CITS.2017.8035314
26 G Linden, B Smith, J York. Amazon. com recommendations: itemto-item collaborative filtering. IEEE Internet Computing, 2003, 7(1): 76–80
https://doi.org/10.1109/MIC.2003.1167344
27 P Resnick, H R Varian. Recommender systems. Communications of the ACM, 1997, 40(3): 56–58
https://doi.org/10.1145/245108.245121
28 R J Mooney, L Roy. Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries. 2000, 195–204
https://doi.org/10.1145/336597.336662
29 K Bhumichitr, S Channarukul, N Saejiem, R Jiamthapthaksin, K Nongpong. Recommender systems for university elective course recommendation. In: Proceedings of the 14th International Joint Conference on Computer Science and Software Engineering (JCSSE). 2017, 1–5
https://doi.org/10.1109/JCSSE.2017.8025933
30 W Carrer-Neto, M L Hernández-Alcaraz, R Valencia-García, F Garcìa-Sánchez. Social knowledge-based recommender system application to the movies domain. Expert Systems with Applications, 2012, 39(12): 10990–11000
https://doi.org/10.1016/j.eswa.2012.03.025
31 J K Tarus, Z Niu, A Yousif. A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 2017, 72: 37–48
https://doi.org/10.1016/j.future.2017.02.049
32 R Burke. Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction, 2002, 12(4): 331–370
https://doi.org/10.1023/A:1021240730564
33 D E Rumelhart, G E Hinton, R J Williams. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533
https://doi.org/10.1038/323533a0
34 P Baldi, K Hornik. Neural networks and principal component analysis: learning from examples without local minima. Neural Networks, 1989, 2(1): 53–58
https://doi.org/10.1016/0893-6080(89)90014-2
35 M Chen, Z Xu, K Weinberger, F Sha. Marginalized denoising autoencoders for domain adaptation. In: Proceedings of the 29th International Conference on Machine Learning. 2012, 1627–1634
36 S Zhang, L Yao, X Xu. Autosvd++: an efficient hybrid collaborative filtering model via contractive auto-encoders. 2017, arXiv preprint arXiv:1704.00551
https://doi.org/10.1145/3077136.3080689
37 N Japkowicz, S J Hanson, M A Gluck. Nonlinear autoassociation is not equivalent to PCA. Neural Computation, 2000, 12(3): 531–545
https://doi.org/10.1162/089976600300015691
38 G E Hinton, R R Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504–507
https://doi.org/10.1126/science.1127647
39 D P Bertsekas, J N Tsitsiklis. Gradient convergence in gradient methods with errors. Society for Industrial and Applied Mathematics Journal on Optimization, 1999, 10(3): 627–642
https://doi.org/10.1137/S1052623497331063
40 Y Takane, F W Young, J D Leeuw. Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features. Psychometrika, 1977, 42(1): 7–67
https://doi.org/10.1007/BF02293745
41 P Vincent, H Larochelle, Y Bengio, P A Manzagol. Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning. 2008, 1096–1103
https://doi.org/10.1145/1390156.1390294
42 G E Hinton, S Osindero, Y M Teh. A fast learning algorithm for deep belief nets. Neural Computation, 2014, 18(7): 1527–1554
https://doi.org/10.1162/neco.2006.18.7.1527
43 Y Bengio, P Lamblin, D Popovici, H Larochelle. Greedy layer-wise training of deep networks. In: Proceedings of the International Conference on Neural Information Processing Systems. 2007, 153–160
44 P Vincent, H Larochelle, I Lajoie, Y Bengio. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010, 11(12): 3371–3408
45 M Chen, K Weinberger, F Sha, Y Bengio. Marginalized denoising auto-encoders for nonlinear representations. In: Proceedings of the International Conference on Machine Learning. 2014, 1476–1484
46 D P Kingma, M Welling. Auto-encoding variational bayes. In: Proceedings of the 2nd International Conference on Learning Representations (ICLR). 2013
47 M W Gardner, S R Dorling. Artificial neural networks (the multilayer perceptron)–a review of applications in the atmospheric sciences. Atmospheric Environment, 1998, 32(14-15): 2627–2636
https://doi.org/10.1016/S1352-2310(97)00447-0
48 Y LeCun, Y Bengio, G Hinton. Deep learning. Nature, 2015, 521(7553): 436
https://doi.org/10.1038/nature14539
49 K Yehuda, B Robert, V Chris. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30–37
https://doi.org/10.1109/MC.2009.263
50 A Mnih, R R Salakhutdinov. Probabilistic matrix factorization. In: Proceedings of the 20th International Conference on Neural Information Processing Systems. 2007, 1257–1264
51 S Rendle. Factorization machines. In: Proceedings of the 10th International Conference on Data Mining (ICDM). 2010, 995–1000
https://doi.org/10.1109/ICDM.2010.127
52 Y Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008, 426–434
https://doi.org/10.1145/1401890.1401944
53 P S Huang, X He, J Gao, L Deng, A Acero. Learning deep structured semantic models for Web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. 2013, 2333–2338
https://doi.org/10.1145/2505515.2505665
54 I Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, A Courville, Y Bengio. Generative adversarial nets. In: Proceedings of the International Conference on Neural Information Processing Systems. 2014, 2672–2680
55 Y Ouyang, W Liu, W Rong, Z Xiong. Autoencoder-based collaborative filtering. In: Processing of the International Conference on Neural Information. 2014, 284–291
https://doi.org/10.1007/978-3-319-12643-2_35
56 S Sedhain, A K Menon, S Sanner, L Xie. Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web. 2015, 111–112
https://doi.org/10.1145/2740908.2742726
57 F Strub, J Mary. Collaborative filtering with stacked denoising autoencoders and sparse inputs. In: Proceedings of the NIPS Workshop on Machine Learning for eCommerce. 2015
58 F Strub, J Mary, R Gaudel. Hybrid collaborative filtering with autoencoders. 2016, arXiv preprint arXiv:1603.00806
59 Y Wu, C DuBois, A X Zheng, M Ester. Collaborative denoising autoencoders for top-n recommender systems. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 2016, 153–162
https://doi.org/10.1145/2835776.2835837
60 B Yi, X Shen, Z Zhang, J Shu, H Liu. Expanded autoencoder recommendation framework and its application in movie recommendation. In: Proceedings of the 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA). 2016, 298–303
https://doi.org/10.1109/SKIMA.2016.7916236
61 Y Pan, F He, H Yu. Trust-aware collaborative denoising auto-encoder for top-n recommendation. 2017, arXiv preprint arXiv:1703.01760
62 S Zhang, L Yao, X Xu, S Wang, L Zhu. Hybrid collaborative recommendation via semi-autoencoder. In: Proceedings of the International Conference on Neural Information. 2017, 185–193
https://doi.org/10.1007/978-3-319-70087-8_20
63 J W Lee, J Lee. IDAE: imputation-boosted denoising autoencoder for collaborative filtering. In: Proceedings of the 2017 ACM Conference on Information and Knowledge Management (CIKM). 2017, 2143–2146
https://doi.org/10.1145/3132847.3133158
64 F Zhuang, D Luo, N J Yuan. Representation learning with pair-wise constraints for collaborative ranking. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining. 2017, 567–575
https://doi.org/10.1145/3018661.3018720
65 H Liang, T Baldwin. A probabilistic rating auto-encoder for personalized recommender systems. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015, 1863–1866
https://doi.org/10.1145/2806416.2806633
66 Y Suzuki, T Ozaki. Stacked denoising autoencoder-based deep collaborative filtering using the change of similarity. In: Proceedings of the 31st International Conference on Information Networking and Applications Workshops (WAINA). 2017, 498–502
https://doi.org/10.1109/WAINA.2017.72
67 A Majumdar, A Jain. Cold-start, warm-start and everything in between: an autoencoder based approach to recommendation. In: Proceedings of International Joint Conference on Neural Networks. 2017, 3656–3663
https://doi.org/10.1109/IJCNN.2017.7966316
68 M Krstic, M Bjelica. Personalized program guide based on one-class classifier. IEEE Transactions on Consumer Electronics. 2016, 62(2): 175–181
https://doi.org/10.1109/TCE.2016.7514717
69 H Wang, X Shi, D Y Yeung. Relational stacked denoising autoencoder for tag recommendation. In: Proceedings of AAAI Conference on Artificial Intelligence. 2015, 3052–3058
70 H Wang, N Wang, D Y Yeung. Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015, 1235–1244
https://doi.org/10.1145/2783258.2783273
71 D Liang, R G Krishnan, M D Hoffman, T Jebara. Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 Conference on World Wide Web. 2018, 689–698
https://doi.org/10.1145/3178876.3186150
72 H Ying, L Chen, Y Xiong, J Wu. Collaborative deep ranking: a hybrid pair-wise recommendation algorithm with implicit feedback. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2016, 555–567
https://doi.org/10.1007/978-3-319-31750-2_44
73 F Zhuang, Z Zhang, M Qian, C Shi, X Xie, Q He. Representation learning via dual-autoencoder for recommendation. Neural Networks, 2017, 90: 83–89
https://doi.org/10.1016/j.neunet.2017.03.009
74 B Bai, Y Fan, W Tan, J Zhang. Dltsr: a deep learning framework for recommendation of long-tailWeb services. IEEE Transactions on Services Computing, 2017, 99: 1
https://doi.org/10.1109/TSC.2017.2681666
75 X Dong, L Yu, Z Wu, Y Sun, L Yuan, F Zhang. A hybrid collaborative filtering model with deep structure for recommender systems. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017, 1309–1315
76 T T Nguyen, H W Lauw. Collaborative topic regression with denoising autoencoder for content and community co-representation. In: Proceedings of the ACM Conference on Information and Knowledge Management. 2017, 2231–2234
https://doi.org/10.1145/3132847.3133128
77 K Mori, S Ito, T Harada, R Thawonmas, K Kim. Feature extraction of gameplays for similarity calculation in gameplay recommendation. In: Proceedings of the 10th IEEE International Workshop on Computational Intelligence and Applications. 2017, 171–176
https://doi.org/10.1109/IWCIA.2017.8203580
78 Y Zuo, J Zeng, M Gong, L Jiao. Tag-aware recommender systems based on deep neural networks. Neurocomputing, 2016, 204: 51–60
https://doi.org/10.1016/j.neucom.2015.10.134
79 J Wei, J He, K Chen, Y Zhou, Z Tang. Collaborative filtering and deep learning based hybrid recommendation for cold start problem. In: Proceedings of the 14th IEEE International Conference on Dependable, Autonomic and Secure Computing. 2016, 874–877
https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.149
80 J Wei, J He, K Chen, Y Zhou, Z Tang. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 2017, 69: 29–39
https://doi.org/10.1016/j.eswa.2016.09.040
81 S Cao, N Yang, Z Liu. Online news recommender based on stacked auto-encoder. In: Proceedings of the 16th IEEE/ACIS International Conference on Computer and Information Science (ICIS). 2017, 721–726
https://doi.org/10.1109/ICIS.2017.7960088
82 B Niu, D Zou, Y Niu. A stacked denoising autoencoders based collaborative approach for recommender system. In: Proceedings of the International Symposium on Parallel Architecture, Algorithm and Programming. 2017, 172–181
https://doi.org/10.1007/978-981-10-6442-5_15
83 S Deng, L Huang, G Xu, X Wu, Z Wu. On deep learning for trustaware recommendations in social networks. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(5): 1164–1177
https://doi.org/10.1109/TNNLS.2016.2514368
84 Y Qian, L Wai. Review-aware answer prediction for product-related questions incorporating aspects. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018, 691–699
85 H Wang, X Shi, D Y Yeung. Collaborative recurrent autoencoder: recommend while learning to fill in the blanks. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016, 415–423
86 W Lee, K Song, I C Moon. Augmented variational autoencoders for collaborative filtering with auxiliary information. In: Proceedings of the ACM Conference on Information and Knowledge Management. 2017, 1139–1148
https://doi.org/10.1145/3132847.3132972
87 V Bellini, V W Anelli, T D Noia, E D Sciascio. Auto-encoding user ratings via knowledge graphs in recommendation scenarios. In: Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems. 2017, 60–66
https://doi.org/10.1145/3125486.3125496
88 S Gu, X Liu, L Cai, J Shen. Fashion coordinates recommendation based on user behavior and visual clothing style. In: Proceedings of the 3rd International Conference on Communication and Information Processing. 2017, 185–189
https://doi.org/10.1145/3162957.3162982
89 R Salakhutdinov, A Mnih, G Hinton. Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning. 2007, 791–798
https://doi.org/10.1145/1273496.1273596
90 V Nair, G E Hinton. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10). 2010, 807–814
91 M Riedmiller, H Braun. A direct adaptive method for faster backpropagation learning: the rprop algorithm. In: Proceedings of the IEEE International Conference on Neural Networks. 1993, 586–591
92 A Zhang, E Wei, B B Parker. Optimal estimation of tidal open boundary conditions using predicted tides and adjoint data assimilation technique. Continental Shelf Research, 2003, 23(11–13): 1055–1070
https://doi.org/10.1016/S0278-4343(03)00105-5
93 M Kim, P Smaragdis. Adaptive denoising autoencoders: a fine-tuning scheme to learn from test mixtures. In: Proceedings of the International Conference on Latent Variable Analysis and Signal Separation. 2015, 100–107
https://doi.org/10.1007/978-3-319-22482-4_12
94 J Duchi, E Hazan, Y Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 2011, 12(7): 2121–2159
95 Huber, J Peter. Robust estimation of a location parameter. The Annals of Mathematical Statistics, 1964, 35(1): 73–101
https://doi.org/10.1214/aoms/1177703732
96 J Ngiam, A Khosla, M Kim, J Nam, H Lee, A Y Ng. Multimodal deep learning. In: Proceedings of the International Conference on Machine Learning (ICML). 2011, 689–696
97 J Bennett, S Lanning. The netflix prize. In: Proceedings of KDD Cup and Workshop. 2007, 35
98 S Nathan, J Tommi. Weighted low-rank approximations. In: Proceedings of the International Conference on Machine Learning. 2003, 720–727
99 D D Lee, H S Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401(6755): 788–791
https://doi.org/10.1038/44565
100 D D Lee, H S Seung. Algorithms for non-negative matrix factorization. In: Proceedings of the International Conference on Neural Information Processing Systems. 2001, 556–562
101 P Arkadiusz. Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop. 2007, 5–8
102 R Salakhutdinov, A Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th International Conference on Machine Learning. 2008, 880–887
https://doi.org/10.1145/1390156.1390267
103 N Srebro, J Rennie, T S Jaakkola. Maximum-margin matrix factorization. In: Proceedings of the International Conference on Neural Information Processing Systems. 2005, 37(2): 1329–1336
104 M Xu, J Zhu, B Zhang. Fast max-margin matrix factorization with data augmentation. In: Proceedings of the International Conference on Machine Learning. 2013, 978–986
105 J Shi, N Wang, Y Xia, D Y Yeung, I King, J Jia. SCMF: sparse covariance matrix factorization for collaborative filtering. In: Proceedings of the International Conference on Artificial Intelligence. 2013, 2705–2711
106 H Ma, D Zhou, C Liu, M R Lyu, I King. Recommender systems with social regularization. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 287–296
107 R P Adams, G E Dahl, I Murray. Incorporating side information in probabilistic matrix factorization with gaussian processes. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence. 2010, 1–9
108 T Zhao, J McAuley, I King. Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. 2014, 261–270
https://doi.org/10.1145/2661829.2661998
109 I Porteous, A U Asuncion, M Welling. Bayesian matrix factorization with side information and dirichlet process mixtures. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010, 563–568
110 Y D Kim, S Choi. Scalable variational Bayesian matrix factorization with side information. In: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics. 2014, 493–502
111 A P Singh, G J Gordon. Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008, 650–658
https://doi.org/10.1145/1401890.1401969
112 S Park, Y D Kim, S Choi. Hierarchical Bayesian matrix factorization with side information. In: Proceedings of the International Joint Conference on Artifical Intelligence. 2013, 1593–1599
113 L Hu, J Cao, G Xu, L Cao, Z Gu, C Zhu. Personalized recommendation via cross-domain triadic factorization. In: Proceedings of the 22nd International Conference on World Wide Web. 2013, 595–606
114 A K Menon, K P Chitrapura, S Garg, D Agarwal, N Kota. Response prediction using collaborative filtering with hierarchies and side-information. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 141–149
https://doi.org/10.1145/2020408.2020436
115 S Li, J Kawale, Y Fu. Predicting user behavior in display advertising via dynamic collective matrix factorization. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2015, 875–878
116 A K Gupta, D K Nagar. Matrix Variate Distributions. Boca Raton: CRC Press. 1999
117 M J F Gales, S S Airey. Product of gaussians for speech recognition. Computer Speech & Language, 2006, 20(1): 22–40
https://doi.org/10.1016/j.csl.2004.12.002
118 H Wang, D Y Yeung. Towards bayesian deep learning: a framework and some existing methods. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12): 3395–3408
https://doi.org/10.1109/TKDE.2016.2606428
119 Y Hu, Y Koren, C Volinsky. Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 263–272
120 R Pan, Y Zhou, B Cao, N N Liu, R Lukose, M Scholz, Q Yang. Oneclass collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 502–511
https://doi.org/10.1109/ICDM.2008.16
121 W Yao, J He, H Wang, Y Zhang, J Cao. Collaborative topic ranking: Leveraging item meta-data for sparsityreduction. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2015, 374–380
122 S Rendle, C Freudenthaler, Z Gantner, Y Zhang, J Cao. BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2009, 452–461
123 E H Chi, T Mytkowicz. Understanding navigability of social tagging systems. In: Proceedings of ACM CHI Conference. 2007
124 A Hotho, R Jäschke, C Schmitz, G Stumme. Information retrieval in folksonomies: search and ranking. In: Proceedings of the European Conference on the Semantic Web: Research and Applications. 2006, 411–426
https://doi.org/10.1007/11762256_31
125 H Lee, A Battle, R Raina, A Y Ng. Efficient sparse coding algorithms. In: Proceedings of the International Conference on Neural Information Processing Systems. 2007, 801–808
126 F Ricci, L Rokach, B Shapira. Introduction to Recommender Systems Handbook. Springer, Boston, MA, 2011, 1–35
https://doi.org/10.1007/978-0-387-85820-3_1
127 Y Koren. Collaborative filtering with temporal dynamics. Communications of the ACM, 2010, 53(4): 89–97
https://doi.org/10.1145/1721654.1721677
128 D Zhang, C H Hsu, M Chen, Q Chen, H Xiong, J Uoret. Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems. IEEE Transactions on Emerging Topics in Computing, 2014, 2(2): 239–250
https://doi.org/10.1109/TETC.2013.2283233
129 S Rifai, P Vincent, X Muller, X Glorot, Y Bengio. Contractive autoencoders: explicit invariance during featureextraction. In: Proceedings of the 28th International Conference onMachine Learning. 2011, 833–840
130 Y Lin, Z Liu, M Sun, Y Liu, X Zhu. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2015, 2181–2187
131 K Cho, B Van Merriënboer, C Gulcehre, D Bahdanau, F Bougares, H Schwenk, Y Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. 2014, arXiv preprint arXiv:1406.1078
https://doi.org/10.3115/v1/D14-1179
132 S Hochreiter, S Jürgen. Long short-term memory. Neural Computation, 1997, 9(8): 1735–1780
https://doi.org/10.1162/neco.1997.9.8.1735
133 C K Sønderby, T Raiko, L Maaløe, S K Sønderby, O Winter. Ladder variational autoencoders. In: Proceedings of the Neural Information Processing Systems. 2016, 3738–3746
134 C K Hsieh, L Yang, H Wei, M Naaman, D Estrin. Immersive recommendation: news and event recommendations using personal digital traces. In: Proceedings of International Conference on World Wide Web. 2016, 51–62
https://doi.org/10.1145/2872427.2883006
135 L Yao, Q Z Sheng, A H H Ngu, X Li. Things of interest recommendation by leveraging heterogeneous relations in the internet of things. Acm Transactions on Internet Technology, 2016, 16(2): 9
https://doi.org/10.1145/2837024
136 Y Burda, R Grosse, R Salakhutdinov. Importance weighted autoencoders. Computer Science, 2015
137 J T Rolfe. Discrete variational autoencoders. In: Proceedings of International Conference on Learning Representations. 2017
138 R Collobert, J Weston. A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of International Conference on Machine Learning. 2008, 160–167
https://doi.org/10.1145/1390156.1390177
139 L Deng, D Yu. Deep learning: methods and applications. Foundations & Trends in Signal Processing, 2014, 7(3): 197–387
https://doi.org/10.1561/2000000039
140 H Dai, Y Wang, R Trivedi, L Song. Recurrent coevolutionary latent feature processes for continuous-time recommendation. In: Proceedings of the Workshop on Deep Learning for Recommender Systems. 2016, 29–34
https://doi.org/10.1145/2988450.2988451
141 Y Wang, D Nan, R Trivedi, L Song. Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016, 4554–4562
142 J L Herlocker, J A Konstan, J Riedl. Explaining collaborative filtering recommendations. In: Proceedings of ACM Conference on Computer Supported Cooperative Work. 2000, 241–250
https://doi.org/10.1145/358916.358995
143 F Gedikli, D Jannach, M Ge. How should I explain? a comparison of different explanation types for recommender systems. International Journal of Human- Computer Studies, 2014, 72(4): 367–382
https://doi.org/10.1016/j.ijhcs.2013.12.007
144 H Cramer, V Evers, S Ramlal, M V Someren, L Rutledge, N Stash, L Aroyo. The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-Adapted Interaction, 2008, 18(5): 455–496
https://doi.org/10.1007/s11257-008-9051-3
145 G Friedrich, M Zanker. A taxonomy for generating explanations in recommender systems. AI Magazine, 2011, 32(3): 90–98
https://doi.org/10.1609/aimag.v32i3.2365
146 R Sharma, S Ray. Explanations in recommender systems: an overview. International Journal of Business Information Systems, 2016, 23(2): 248
https://doi.org/10.1504/IJBIS.2016.078909
147 C Wang, D M Blei. Collaborative topic modeling for recommending scientific articles. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 448–456
https://doi.org/10.1145/2020408.2020480
148 W X Zhao, J Wang, Y He, J R Wen, E Y Chang, X Li. Mining product adopter information from online reviews for improving product recommendation. Acm Transactions on Knowledge Discovery from Data, 2016, 10(3): 1–23
https://doi.org/10.1145/2842629
149 J Chen, H Zhang, X He, L Nie, W Liu, T Chua. Attentive collaborative filtering: multimedia recommendation with item- and componentlevel attention. In: Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017, 335–344
https://doi.org/10.1145/3077136.3080797
150 R Gemulla, E Nijkamp, P J Haas, Y Sismanis. Large-scale matrix factorization with distributed stochastic gradient descent. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 69–77
https://doi.org/10.1145/2020408.2020426
151 S Y Zhao, W J Li. Fast asynchronous parallel stochastic gradient descent: a lock-free approach with convergence guarantee. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016, 2379–2385
152 M Ge, C Delgado-Battenfeld, D Jannach. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of ACM Conference on Recommender Systems. 2010, 257–260
153 M M Khan, R Ibrahim, I Ghani. Cross domain recommender systems: a systematic literature review. ACM Computing Surveys, 2017, 50(3): 1–34
https://doi.org/10.1145/3073565
154 B Mobasher, R Burke, R Bhaumik, C Williams. Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACMTransactions on Internet Technology, 2007, 7(4): 23
https://doi.org/10.1145/1278366.1278372
155 S Varges, P Castells. Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of ACM Conference on Recommender Systems. 2011, 109–116
https://doi.org/10.1145/2043932.2043955
[1] Article highlights Download
[1] Huiying ZHANG, Yu ZHANG, Xin GENG. Practical age estimation using deep label distribution learning[J]. Front. Comput. Sci., 2021, 15(3): 153318-.
[2] Genan DAI, Xiaoyang HU, Youming GE, Zhiqing NING, Yubao LIU. Attention based simplified deep residual network for citywide crowd flows prediction[J]. Front. Comput. Sci., 2021, 15(2): 152317-.
[3] Syed Farooq ALI, Muhammad Aamir KHAN, Ahmed Sohail ASLAM. Fingerprint matching, spoof and liveness detection: classification and literature review[J]. Front. Comput. Sci., 2021, 15(1): 151310-.
[4] Yuling MA, Chaoran CUI, Jun YU, Jie GUO, Gongping YANG, Yilong YIN. Multi-task MIML learning for pre-course student performance prediction[J]. Front. Comput. Sci., 2020, 14(5): 145313-.
[5] Chune LI, Yongyi MAO, Richong ZHANG, Jinpeng HUAI. A revisit to MacKay algorithm and its application to deep network compression[J]. Front. Comput. Sci., 2020, 14(4): 144304-.
[6] Tao LIAN, Lin DU, Mingfu ZHAO, Chaoran CUI, Zhumin CHEN, Jun MA. Evaluating and improving the interpretability of item embeddings using item-tag relevance information[J]. Front. Comput. Sci., 2020, 14(3): 143603-.
[7] Yiteng PAN, Fazhi HE, Haiping YU. A correlative denoising autoencoder to model social influence for top-N recommender system[J]. Front. Comput. Sci., 2020, 14(3): 143301-.
[8] Lu LIU, Shang WANG. Meta-path-based outlier detection in heterogeneous information network[J]. Front. Comput. Sci., 2020, 14(2): 388-403.
[9] Tian WANG, Meina QIAO, Aichun ZHU, Guangcun SHAN, Hichem SNOUSSI. Abnormal event detection via the analysis of multi-frame optical flow information[J]. Front. Comput. Sci., 2020, 14(2): 304-313.
[10] Xu-Ying LIU, Sheng-Tao WANG, Min-Ling ZHANG. Transfer synthetic over-sampling for class-imbalance learning with limited minority class data[J]. Front. Comput. Sci., 2019, 13(5): 996-1009.
[11] Satoshi MIYAZAWA, Xuan SONG, Tianqi XIA, Ryosuke SHIBASAKI, Hodaka KANEDA. Integrating GPS trajectory and topics from Twitter stream for human mobility estimation[J]. Front. Comput. Sci., 2019, 13(3): 460-470.
[12] Qianjun ZHANG, Lei ZHANG. Convolutional adaptive denoising autoencoders for hierarchical feature extraction[J]. Front. Comput. Sci., 2018, 12(6): 1140-1148.
[13] Shuaiqiang WANG, Yilong YIN. Polygene-based evolutionary algorithms with frequent pattern mining[J]. Front. Comput. Sci., 2018, 12(5): 950-965.
[14] Lili HUANG, Jiefeng PENG, Ruimao ZHANG, Guanbin LI, Liang LIN. Learning deep representations for semantic image parsing: a comprehensive overview[J]. Front. Comput. Sci., 2018, 12(5): 840-857.
[15] Bo SUN, Haiyan CHEN, Jiandong WANG, Hua XIE. Evolutionary under-sampling based bagging ensemble method for imbalanced data classification[J]. Front. Comput. Sci., 2018, 12(2): 331-350.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed