A survey of autoencoder-based recommender systems

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

References

[1] Soghra L, 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] Zhang S, Yao L, Sun A, Tay Y. Deep learning based recommender system: a survey and new perspectives. 2017, arXiv preprint arXiv:1707.07435

[3] Cacheda F, Carneiro V, Fernández D, Formoso V. 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

[4] Nguyen A T, Denos N, Berrut C. 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

[5] Rashid A M, Albert I, Cosley D, Lam S K, McNee S W, Konstan J A, Riedl J. 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

[6] Ebesu T, Fang Y. Neural semantic personalized ranking for item coldstart recommendation. Information Retrieval Journal, 2017, 20(2): 109–131

[7] Chow R, Jin H, Knijnenburg B, Saldamli G. Differential data analysis for recommender systems. In: Proceedings of the 7th ACM Conference on Recommender Systems. 2013, 323–326

[8] Gomez-Uribe C A, Hunt N. The netflix recommender system: algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 2015, 6(4): 1–19

[9] Sottocornola G, Stella F, Zanker M, Canonaco F. Towards a deep learning model for hybrid recommendation. In: Proceedings of the International Conference on Web Intelligence. 2017, 1260–1264

[10] Yan S, Lin K J, Zheng X, Zhang W, Feng X. 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

[11] Wu H, Zhang Z, Yue K, Zhang B, Zhu R. Content embedding regularized matrix factorization for recommender systems. In: Proceedings of the 2017 IEEE International Congress on Big Data (BigData Congress). 2017, 209–215

[12] McAuley J, Leskovec J. Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems. 2013, 165–172

[13] Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge. MA: MIT Press, 2016

[14] Peng X, Li Y, Wei X, Luo J, Marphey Y L. Traffic sign recognition with transfer learning. In: Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI). 2017, 1–7

[15] Dehghan A, Ortiz E G, Villegas R, Shah M. 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

[16] Lu X, Yu T, Matsuda S, Hori C. Speech enhancement based on deep denoising autoencoder. Interspeech, 2013, 436–440

[17] Li X, She J. Collaborative variational autoencoder for recommender systems. In: Proceedings of the 23rd ACM International Conference on Knowledge Discovery and Data Mining. 2017, 305–314

[18] Zhang F, Yuan N J, Lian D, Xie X, Ma W Y. Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM International Conference on Knowledge Discovery and Data Mining. 2016, 353–362

[19] Unger M. Latent context-aware recommender systems. In: Proceedings of the 9th ACM Conference on Recommender Systems. 2015, 383–386

[20] Unger M, Bar A, Shapira B, Rokach L. Towards latent contextaware recommendation systems. Knowledge-Based Systems, 2016, 104: 165–178

[21] Li X, She J. Relational variational autoencoder for link prediction with multimedia data. In: Proceedings of the Thematic Workshops of ACM Multimedia. 2017, 93–100

[22] Li S, Kawale J, Fu Y. Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015, 811–820

[23] Rafailidis D, Crestani F. Recommendation with social relationships via deep learning. In: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval. 2017, 151–158

[24] Adomavicius G, Tuzhilin A. 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

[25] Lu J, Guo Y, Mi Z, Yang Y. 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

[26] Linden G, Smith B, York J. Amazon. com recommendations: itemto-item collaborative filtering. IEEE Internet Computing, 2003, 7(1): 76–80

[27] Resnick P, Varian H R. Recommender systems. Communications of the ACM, 1997, 40(3): 56–58

[28] Mooney R J, Roy L. Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries. 2000, 195–204

[29] Bhumichitr K, Channarukul S, Saejiem N, Jiamthapthaksin R, Nongpong K. 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

[30] Carrer-Neto W, Hernández-Alcaraz M L, Valencia-García R, Garcìa-Sánchez F. Social knowledge-based recommender system application to the movies domain. Expert Systems with Applications, 2012, 39(12): 10990–11000

[31] Tarus J K, Niu Z, Yousif A. A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 2017, 72: 37–48

[32] Burke R. Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction, 2002, 12(4): 331–370

[33] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533

[34] Baldi P, Hornik K. Neural networks and principal component analysis: learning from examples without local minima. Neural Networks, 1989, 2(1): 53–58

[35] Chen M, Xu Z, Weinberger K, Sha F. Marginalized denoising autoencoders for domain adaptation. In: Proceedings of the 29th International Conference on Machine Learning. 2012, 1627–1634

[36] Zhang S, Yao L, Xu X. Autosvd++: an efficient hybrid collaborative filtering model via contractive auto-encoders. 2017, arXiv preprint arXiv:1704.00551

[37] Japkowicz N, Hanson S J, Gluck M A. Nonlinear autoassociation is not equivalent to PCA. Neural Computation, 2000, 12(3): 531–545

[38] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504–507

[39] Bertsekas D P, Tsitsiklis J N. Gradient convergence in gradient methods with errors. Society for Industrial and Applied Mathematics Journal on Optimization, 1999, 10(3): 627–642

[40] Takane Y, Young F W, Leeuw J D. Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features. Psychometrika, 1977, 42(1): 7–67

[41] Vincent P, Larochelle H, Bengio Y, Manzagol P A. Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning. 2008, 1096–1103

[42] Hinton G E, Osindero S, Teh Y M. A fast learning algorithm for deep belief nets. Neural Computation, 2014, 18(7): 1527–1554

[43] Bengio Y, Lamblin P, Popovici D, Larochelle H. Greedy layer-wise training of deep networks. In: Proceedings of the International Conference on Neural Information Processing Systems. 2007, 153–160

[44] Vincent P, Larochelle H, Lajoie I, Bengio Y. 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] Chen M, Weinberger K, Sha F, Bengio Y. Marginalized denoising auto-encoders for nonlinear representations. In: Proceedings of the International Conference on Machine Learning. 2014, 1476–1484

[46] Kingma D P, Welling M. Auto-encoding variational bayes. In: Proceedings of the 2nd International Conference on Learning Representations (ICLR). 2013

[47] Gardner M W, Dorling S R. Artificial neural networks (the multilayer perceptron)–a review of applications in the atmospheric sciences. Atmospheric Environment, 1998, 32(14-15): 2627–2636

[48] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436

[49] Yehuda K, Robert B, Chris V. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30–37

[50] Mnih A, Salakhutdinov R R. Probabilistic matrix factorization. In: Proceedings of the 20th International Conference on Neural Information Processing Systems. 2007, 1257–1264

[51] Rendle S. Factorization machines. In: Proceedings of the 10th International Conference on Data Mining (ICDM). 2010, 995–1000

[52] Koren Y. 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

[53] Huang P S, He X, Gao J, Deng L, Acero A. 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

[54] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proceedings of the International Conference on Neural Information Processing Systems. 2014, 2672–2680

[55] Ouyang Y, Liu W, Rong W, Xiong Z. Autoencoder-based collaborative filtering. In: Processing of the International Conference on Neural Information. 2014, 284–291

[56] Sedhain S, Menon A K, Sanner S, Xie L. Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web. 2015, 111–112

[57] Strub F, Mary J. Collaborative filtering with stacked denoising autoencoders and sparse inputs. In: Proceedings of the NIPS Workshop on Machine Learning for eCommerce. 2015

[58] Strub F, Mary J, Gaudel R. Hybrid collaborative filtering with autoencoders. 2016, arXiv preprint arXiv:1603.00806

[59] Wu Y, DuBois C, Zheng A X, Ester M. 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

[60] Yi B, Shen X, Zhang Z, Shu J, Liu H. 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

[61] Pan Y, He F, Yu H. Trust-aware collaborative denoising auto-encoder for top-n recommendation. 2017, arXiv preprint arXiv:1703.01760

[62] Zhang S, Yao L, Xu X, Wang S, Zhu L. Hybrid collaborative recommendation via semi-autoencoder. In: Proceedings of the International Conference on Neural Information. 2017, 185–193

[63] Lee J W, Lee J. IDAE: imputation-boosted denoising autoencoder for collaborative filtering. In: Proceedings of the 2017 ACM Conference on Information and Knowledge Management (CIKM). 2017, 2143–2146

[64] Zhuang F, Luo D, Yuan N J. 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

[65] Liang H, Baldwin T. 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

[66] Suzuki Y, Ozaki T. 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

[67] Majumdar A, Jain A. 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

[68] Krstic M, Bjelica M. Personalized program guide based on one-class classifier. IEEE Transactions on Consumer Electronics. 2016, 62(2): 175–181

[69] Wang H, Shi X, Yeung D Y. Relational stacked denoising autoencoder for tag recommendation. In: Proceedings of AAAI Conference on Artificial Intelligence. 2015, 3052–3058

[70] Wang H, Wang N, Yeung D Y. Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015, 1235–1244

[71] Liang D, Krishnan R G, Hoffman M D, Jebara T. Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 Conference on World Wide Web. 2018, 689–698

[72] Ying H, Chen L, Xiong Y, Wu J. 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

[73] Zhuang F, Zhang Z, Qian M, Shi C, Xie X, He Q. Representation learning via dual-autoencoder for recommendation. Neural Networks, 2017, 90: 83–89

[74] Bai B, Fan Y, Tan W, Zhang J. Dltsr: a deep learning framework for recommendation of long-tailWeb services. IEEE Transactions on Services Computing, 2017, 99: 1

[75] Dong X, Yu L, Wu Z, Sun Y, Yuan L, Zhang F. 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] Nguyen T T, Lauw H W. 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

[77] Mori K, Ito S, Harada T, Thawonmas R, Kim K. 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

[78] Zuo Y, Zeng J, Gong M, Jiao L. Tag-aware recommender systems based on deep neural networks. Neurocomputing, 2016, 204: 51–60

[79] Wei J, He J, Chen K, Zhou Y, Tang Z. 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

[80] Wei J, He J, Chen K, Zhou Y, Tang Z. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 2017, 69: 29–39

[81] Cao S, Yang N, Liu Z. 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

[82] Niu B, Zou D, Niu Y. 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

[83] Deng S, Huang L, Xu G, Wu X, Wu Z. On deep learning for trustaware recommendations in social networks. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(5): 1164–1177

[84] Qian Y, Wai L. 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] Wang H, Shi X, Yeung D Y. 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] Lee W, Song K, Moon I C. Augmented variational autoencoders for collaborative filtering with auxiliary information. In: Proceedings of the ACM Conference on Information and Knowledge Management. 2017, 1139–1148

[87] Bellini V, Anelli V W, Noia T D, Sciascio E D. 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

[88] Gu S, Liu X, Cai L, Shen J. 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

[89] Salakhutdinov R, Mnih A, Hinton G. Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning. 2007, 791–798

[90] Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10). 2010, 807–814

[91] Riedmiller M, Braun H. 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] Zhang A, Wei E, Parker B B. Optimal estimation of tidal open boundary conditions using predicted tides and adjoint data assimilation technique. Continental Shelf Research, 2003, 23(11–13): 1055–1070

[93] Kim M, Smaragdis P. 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

[94] Duchi J, Hazan E, Singer Y. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 2011, 12(7): 2121–2159

[95] Huber, Peter J. Robust estimation of a location parameter. The Annals of Mathematical Statistics, 1964, 35(1): 73–101

[96] Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng A Y. Multimodal deep learning. In: Proceedings of the International Conference on Machine Learning (ICML). 2011, 689–696

[97] Bennett J, Lanning S. The netflix prize. In: Proceedings of KDD Cup and Workshop. 2007, 35

[98] Nathan S, Tommi J. Weighted low-rank approximations. In: Proceedings of the International Conference on Machine Learning. 2003, 720–727

[99] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401(6755): 788–791

[100] Lee D D, Seung H S. Algorithms for non-negative matrix factorization. In: Proceedings of the International Conference on Neural Information Processing Systems. 2001, 556–562

[101] Arkadiusz P. Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop. 2007, 5–8

[102] Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th International Conference on Machine Learning. 2008, 880–887

[103] Srebro N, Rennie J, Jaakkola T S. Maximum-margin matrix factorization. In: Proceedings of the International Conference on Neural Information Processing Systems. 2005, 37(2): 1329–1336

[104] Xu M, Zhu J, Zhang B. Fast max-margin matrix factorization with data augmentation. In: Proceedings of the International Conference on Machine Learning. 2013, 978–986

[105] Shi J, Wang N, Xia Y, Yeung D Y, King I, Jia J. SCMF: sparse covariance matrix factorization for collaborative filtering. In: Proceedings of the International Conference on Artificial Intelligence. 2013, 2705–2711

[106] Ma H, Zhou D, Liu C, Lyu M R, King I. Recommender systems with social regularization. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 287–296

[107] Adams R P, Dahl G E, Murray I. 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] Zhao T, McAuley J, King I. 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

[109] Porteous I, Asuncion A U, Welling M. Bayesian matrix factorization with side information and dirichlet process mixtures. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010, 563–568

[110] Kim Y D, Choi S. Scalable variational Bayesian matrix factorization with side information. In: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics. 2014, 493–502

[111] Singh A P, Gordon G J. Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008, 650–658

[112] Park S, Kim Y D, Choi S. Hierarchical Bayesian matrix factorization with side information. In: Proceedings of the International Joint Conference on Artifical Intelligence. 2013, 1593–1599

[113] Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C. Personalized recommendation via cross-domain triadic factorization. In: Proceedings of the 22nd International Conference on World Wide Web. 2013, 595–606

[114] Menon A K, Chitrapura K P, Garg S, Agarwal D, Kota N. 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

[115] Li S, Kawale J, Fu Y. 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] Gupta A K, Nagar D K. Matrix Variate Distributions. Boca Raton: CRC Press. 1999

[117] Gales M J F, Airey S S. Product of gaussians for speech recognition. Computer Speech & Language, 2006, 20(1): 22–40

[118] Wang H, Yeung D Y. Towards bayesian deep learning: a framework and some existing methods. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12): 3395–3408

[119] Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 263–272

[120] Pan R, Zhou Y, Cao B, Liu N N, Lukose R, Scholz M, Yang Q. Oneclass collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 502–511

[121] Yao W, He J, Wang H, Zhang Y, Cao J. Collaborative topic ranking: Leveraging item meta-data for sparsityreduction. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2015, 374–380

[122] Rendle S, Freudenthaler C, Gantner Z, Zhang Y, Cao J. BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2009, 452–461

[123] Chi E H, Mytkowicz T. Understanding navigability of social tagging systems. In: Proceedings of ACM CHI Conference. 2007

[124] Hotho A, Jäschke R, Schmitz C, Stumme G. Information retrieval in folksonomies: search and ranking. In: Proceedings of the European Conference on the Semantic Web: Research and Applications. 2006, 411–426

[125] Lee H, Battle A, Raina R, Ng A Y. Efficient sparse coding algorithms. In: Proceedings of the International Conference on Neural Information Processing Systems. 2007, 801–808

[126] Ricci F, Rokach L, Shapira B. Introduction to Recommender Systems Handbook. Springer, Boston, MA, 2011, 1–35

[127] Koren Y. Collaborative filtering with temporal dynamics. Communications of the ACM, 2010, 53(4): 89–97

[128] Zhang D, Hsu C H, Chen M, Chen Q, Xiong H, Uoret J. 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

[129] Rifai S, Vincent P, Muller X, Glorot X, Bengio Y. Contractive autoencoders: explicit invariance during featureextraction. In: Proceedings of the 28th International Conference onMachine Learning. 2011, 833–840

[130] Lin Y, Liu Z, Sun M, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2015, 2181–2187

[131] Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. 2014, arXiv preprint arXiv:1406.1078

[132] Hochreiter S, Jürgen S. Long short-term memory. Neural Computation, 1997, 9(8): 1735–1780

[133] Sønderby C K, Raiko T, Maaløe L, Sønderby S K, Winter O. Ladder variational autoencoders. In: Proceedings of the Neural Information Processing Systems. 2016, 3738–3746

[134] Hsieh C K, Yang L, Wei H, Naaman M, Estrin D. Immersive recommendation: news and event recommendations using personal digital traces. In: Proceedings of International Conference on World Wide Web. 2016, 51–62

[135] Yao L, Sheng Q Z, Ngu A H H, Li X. Things of interest recommendation by leveraging heterogeneous relations in the internet of things. Acm Transactions on Internet Technology, 2016, 16(2): 9

[136] Burda Y, Grosse R, Salakhutdinov R. Importance weighted autoencoders. Computer Science, 2015

[137] Rolfe J T. Discrete variational autoencoders. In: Proceedings of International Conference on Learning Representations. 2017

[138] Collobert R, Weston J. A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of International Conference on Machine Learning. 2008, 160–167

[139] Deng L, Yu D. Deep learning: methods and applications. Foundations & Trends in Signal Processing, 2014, 7(3): 197–387

[140] Dai H, Wang Y, Trivedi R, Song L. Recurrent coevolutionary latent feature processes for continuous-time recommendation. In: Proceedings of the Workshop on Deep Learning for Recommender Systems. 2016, 29–34

[141] Wang Y, Nan D, Trivedi R, Song L. 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] Herlocker J L, Konstan J A, Riedl J. Explaining collaborative filtering recommendations. In: Proceedings of ACM Conference on Computer Supported Cooperative Work. 2000, 241–250

[143] Gedikli F, Jannach D, Ge M. How should I explain? a comparison of different explanation types for recommender systems. International Journal of Human- Computer Studies, 2014, 72(4): 367–382

[144] Cramer H, Evers V, Ramlal S, Someren M V, Rutledge L, Stash N, Aroyo L. 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

[145] Friedrich G, Zanker M. A taxonomy for generating explanations in recommender systems. AI Magazine, 2011, 32(3): 90–98

[146] Sharma R, Ray S. Explanations in recommender systems: an overview. International Journal of Business Information Systems, 2016, 23(2): 248

[147] Wang C, Blei D M. Collaborative topic modeling for recommending scientific articles. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 448–456

[148] Zhao W X, Wang J, He Y, Wen J R, Chang E Y, Li X. Mining product adopter information from online reviews for improving product recommendation. Acm Transactions on Knowledge Discovery from Data, 2016, 10(3): 1–23

[149] Chen J, Zhang H, He X, Nie L, Liu W, Chua T. 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

[150] Gemulla R, Nijkamp E, Haas P J, Sismanis Y. 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

[151] Zhao S Y, Li W J. 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] Ge M, Delgado-Battenfeld C, Jannach D. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of ACM Conference on Recommender Systems. 2010, 257–260

[153] Khan M M, Ibrahim R, Ghani I. Cross domain recommender systems: a systematic literature review. ACM Computing Surveys, 2017, 50(3): 1–34

[154] Mobasher B, Burke R, Bhaumik R, Williams C. Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACMTransactions on Internet Technology, 2007, 7(4): 23

[155] Varges S, Castells P. Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of ACM Conference on Recommender Systems. 2011, 109–116

RIGHTS & PERMISSIONS

2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature