|Table of Contents|

A Survey of Deep Learning Based Recommendation Algorithms(PDF)

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2018年04期
Page:
33-
Research Field:
计算机工程
Publishing date:

Info

Title:
A Survey of Deep Learning Based Recommendation Algorithms
Author(s):
Wang Junshu12Zhang Guoming34Hu Bin12
(1.School of Geography,Nanjing Normal University,Nanjing 210023,China)(2.Key Laboratory of Virtual Geographic Environment of Ministry of Education,Nanjing Normal University,Nanjing 210023,China)(3.Department of Computer Science and Technology,Nanjing University,Nanjing 210023,China)(4.Health Statistics and Information Center of Jiangsu Province,Nanjing 210008,China)
Keywords:
recommender systemsdeep learningcollaborative filteringcontent-based recommendationdynamic recommendationtag-based recommendation
PACS:
TP3-05
DOI:
10.3969/j.issn.1672-1292.2018.04.006
Abstract:
Deep learning technology has recently become a very hot topic in the machine learning field,and has been thoroughly studied and widely used in many fields. The recommender system is an important technology to alleviate information overload. Deep learning has the advantages of learning the intrinsic characteristics of users and items from various complex multidimensional data. How to integrate deep learning into recommender systems to build a model that is more in line with the user preferences and improving recommendation performance is the main research task of deep learning based recommender systems. In this paper,the research and application of deep learning based recommender algorithms are reviewed,and the future research and development trends of integrating deep learning to recommender systems are discussed.

References:

[1] 黄震华,张佳雯,田春岐,等. 基于排序学习的推荐算法研究综述[J]. 软件学报,2016,27(3):691-713.
HUANG Z H,ZHANG J W,TIAN C Q,et al. Survey on learning-to-rank based recommendation algorithms[J]. Journal of software,2016,27(3):691-713.(in Chinese)
[2]DIELEMAN S. Recommending music on spotify with deep learning[EB/OL]. [2014-08-05]. http://benanne.github.io/2014/08/05/spotify-cnns.html.
[3]ELKAHKY A M,SONG Y,HE X. A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//Proceedings of the 24th International Conference on World Wide Web. Florence,Italy,2015.
[4]COVINGTON P,ADAMS J,SARGIN E. Deep neural networks for youtube recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems. Boston,USA,2016.
[5]CHENG H T,KOC L,HARMSEN J,et al. Wide and deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Boston,USA,2016.
[6]RICCI F,ROKACH L,SHAPIRA B,et al. Recommender systems handbook[M]. Berlin:Springer,2011.
[7]刘青文. 基于协同过滤的推荐算法研究[D]. 合肥:中国科学技术大学,2013.
LIU Q W. Research on recommender systems based on collaborative filtering[D]. Hefei:University of Science and Technology of China,2013.(in Chinese)
[8]ZHENG L. A survey and critique of deep learning on recommender systems[EB/OL]. [2018-03-06]. https://bdsc.lab.uic.edu/docs/survey-critique-deep.pdf.
[9]项亮. 推荐系统实践[M]. 北京:人民邮电出版社,2012.
XIANG L. Recommended system practice[M]. Beijing:Posts and Telecom Press,2012.(in Chinese)
[10]MIYAHARA K,PAZZANI M J. Collaborative filtering with the simple Bayesian classifier[M]//MIZOGUCHI R,SLANEY J. PRICAI 2000 topics in artifical intelligence. Berlin:Springer,2000:679-689.
[11]SU X,KHOSHGOFTAAR T M. Collaborative filtering for multi-class data using belief nets algorithms[C]//IEEE International Conference on TOOLS with Artificial Intelligence. Arlington,USA,2006.
[12]CHEE S H S,HAN J,WANG K. RecTree:an efficient collaborative filtering method[C]//Proceedings of Data Warehousing and Knowledge Discovery. Munich,Germany,2001.
[13]UNGAR L H,FOSTER D P. Clustering methods for collaborative filtering[C]//Proceedings of AAAI Workshop on Recommendation Systems. Madison,USA,1998.
[14]CANNY J. Collaborative filtering with privacy via factor analysis[C]//International ACM SIGIR Conference on Research and Development in Information Retrieval. Tampere,Finland,2002.
[15]VUCETIC S,OBRADOVIC Z. Collaborative filtering using a regression-based approach[J]. Knowledge and information systems,2005,7(1):1-22.
[16]LEMIRE D,MACLACHLAN A. Slope one predictors for online rating-based collaborative filtering[C]//Proceedings of the 2005 SIAM International Conference on Data Mining. Newport Beach,USA,2005.
[17]BLEI D M,NG A Y,JORDAN M I. Latent dirichlet allocation[J]. Journal of machine learning research,2003,3:993-1022.
[18]KOREN Y. Factorization meets the neighborhood:a multifaceted collaborative filtering model[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas,USA,2008.
[19]JANNACH D,ZANKER M,FELFERNIG A,et al. Recommender systems:an introduction[M]. Cambridge:Cambridge University Press,2010.
[20]杨文龙. 基于动态集成方法的混合推荐系统研究[D]. 济南:山东大学,2015.
YANG W L. Research on hybrid recommender systems on the dynamically integrated Methodologies[D]. Jinan:Shandong University,2015.(in Chinese)
[21]KOREN Y. The bellkor solution to the netflix grand prize[J]. Netflix prize documentation,2009,81:1-10.
[22]SMYTH B,COTTER P. Personalized electronic program guides for digital TV[J]. AI magazine,2001,22(2):89-98.
[23]SARWAR B M,KONSTAN J A,BORCHERS A,et al. Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system[C]//Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work. Seattle,USA,1998.
[24]CONDLIFF M K,LEWIS D D,MADIGAN D,et al. Bayesian mixed-effects models for recommender systems[C]//ACM SIGIR’99 Workshop on Recommender Systems:Algorithms and Evaluation. Berkeley,USA,1999.
[25]LECUN Y,BENGIO Y,HINTON G. Deep learning[J]. Nature,2015,521(7553):436-444.
[26]孙志远,鲁成祥,史忠植,等. 深度学习研究与进展[J]. 计算机科学,2016,43(2):1-8.SUN Z Y,LU C X,SHI Z Z,et al. Research and advances on deep learning[J]. Computer science,2016,43(2):1-8.(in Chinese)
[27]KRIZHEVSKY A,SUTSKEVER I,HINTON G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. Lake Tahoe,USA,2012.
[28]DONAHUE J,JIA Y,VINYALS O,et al. DeCAF:a deep convolutional activation feature for generic visual recognition[C]//Proceedings of the 31st International Conference on Machine Learning. Beijing,China,2014.
[29]KARPATHY A,TODERICI G,SHETTY S,et al. Large-scale video classification with convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus,USA,2014.
[30]LI J,MONROE W,RITTER A,et al. Deep reinforcement learning for dialogue generation[DB/OL]. [2018-03-06]. https://arxiv.org/abs/1606.01541.
[31]BORDES A,CHOPRA S,WESTON J. Question answering with subgraph embeddings[DB/OL]. [2018-03-06]. https://arxiv.org/abs/1406.3676.
[32]SUTSKEVER I,VINYALS O,LE Q V. Sequence to sequence learning with neural networks[C]//Advances in Neural Information Processing Systems. Montreal,Canada,2014.
[33]MIKOLOV T,DEORAS A,POVEY D,et al. Strategies for training large scale neural network language models[C]//IEEE Workshop on Automatic Speech Recognition and Understanding. Waikoloa,USA,2011.
[34]HINTON G,DENG L,YU D,et al. Deep neural networks for acoustic modeling in speech recognition:the shared views of four research groups[J]. IEEE signal processing magazine,2012,29(6):82-97.
[35]MA J,SHERIDAN R P,LIAW A,et al. Deep neural nets as a method for quantitative structure-activity relationships[J]. Journal of chemical information and modeling,2015,55(2):263-274.
[36]HELMSTAEDTER M,BRIGGMAN K L,TURAGA S C,et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina[J]. Nature,2013,500(7461):168-174.
[37]LEUNG M K K,XIONG H Y,LEE L J,et al. Deep learning of the tissue-regulated splicing code[J]. Bioinformatics,2014,30(12):121-129.
[38]郭丽丽,丁世飞. 深度学习研究进展[J]. 计算机科学,2015,42(5):28-33.
GUO L L,DING S F. Research progress on deep learning[J]. Computer science,2015,42(5):28-33.(in Chinese)
[39]OORD A V D,DIELEMAN S,SCHRAUWEN B. Deep content-based music recommendation[C]//Conference on Neural Information Processing Systems(NIPS 2013). Lake Tahoe,USA,2013.
[40]MCAULEY J,TARGETT C,SHI Q,et al. Image-based recommendations on styles and substitutes[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. Santiage,Chile,2015.
[41]ZHOU J,ALBATAL R,GURRIN C. Applying visual user interest profiles for recommendation and personalisation[C]//International Conference on Multimedia Modeling. Miami,USA,2016.
[42]BANSAL T,BELANGER D,MCCALLUM A. Ask the GRU:multi-task learning for deep text recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems. Boston,USA,2016.
[43]ZANOTTI G,HORVATH M,BARBOSA L N,et al. Infusing collaborative recommenders with distributed representations[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Boston,USA,2016.
[44]CHO K,VAN MERRIENBOER B,BAHDANAU D,et al. On the properties of neural machine translation:encoder-decoder approaches[DB/OL].
[2018-03-06]. https://arxiv.org/abs/1409.1259.
[45]WANG H,WANG N,YEUNG D Y. Collaborative deep learning for recommender systems[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney,Australia,2015.
[46]VINCENT P,LAROCHELLE H,LAJOIE I,et al. Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion[J]. Journal of machine learning research,2010,11:3371-3408.
[47]WANG C,BLEI D M. Collaborative topic modeling for recommending scientific articles[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego,USA,2011.
[48]WEI J,HE J,CHEN K,et al. Collaborative filtering and deep learning based recommendation system for cold start items[J]. Expert systems with applications,2017,69:29-39.
[49]KOREN Y. Collaborative filtering with temporal dynamics[J]. Communications of the ACM,2010,53(4):89-97.
[50]KIM D,PARK C,OH J,et al. Convolutional matrix factorization for document context-aware recommendation[C]//Proceedings of the 10th ACM Conference on Recommender Systems. Boston,USA,2016.
[51]SHEN X,YI B,ZHANG Z,et al. Automatic recommendation technology for learning resources with convolutional neural network[C]//2016 International Symposium on Educational Technology(ISET). Beijing,China,2016.
[52]LI S,KAWALE J,FU Y. Deep collaborative filtering via marginalized denoising auto-encoder[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne,Austrilia,2015.
[53]SHIN D,CETINTAS S,LEE K C,et al. Tumblr blog recommendation with boosted inductive matrix completion[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne,Austrilia,2015.
[54]LEI C,LIU D,LI W,et al. Comparative deep learning of hybrid representations for image recommendations[C]//IEEE Conference on Computer Vision and Pattern Recongnition. Las Vegas,USA,2016.
[55]HUANG P S,HE X,GAO J,et al. Learning deep structured semantic models for web search using clickthrough data[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. San Francisco,USA,2013.
[56]HE X,LIAO L,ZHANG H,et al. Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web. Perth,Australia,2017.
[57]GENG X,ZHANG H,BIAN J,et al. Learning image and user features for recommendation in social networks[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago,Chile,2015.
[58]SALAKHUTDINOV R,MNIH A,HINTON G. Restricted Boltzmann machines for collaborative filtering[C]//Proceedings of the 24th International Conference on Machine Learning. Corralis,USA,2007.
[59]ZHENG Y,TANG B,DING W,et al. A neural autoregressive approach to collaborative filtering[C]//International Conference on Machine Learning. New York,USA,2016.
[60]LAROCHELLE H,MURRAY I. The neural autoregressive distribution estimator[C]//International Conference on Artifcial Intelligence and Statistics. Lauderdale,USA,2011.
[61]GEORGIEV K,NAKOV P. A non-IID framework for collaborative filtering with restricted Boltzmann machines[C]//International Conference on Machine Learning. Atlanta,USA,2013.
[62]SEDHAIN S,MENON A K,SANNER S,et al. Autorec:autoencoders meet collaborative filtering[C]//Proceedings of the 24th International Conference on World Wide Web. Florence,Italy,2015.
[63]STRUB F,GAUDEL R,MARY J. Hybrid recommender system based on autoencoders[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Boston,USA,2016.
[64]WU Y,DUBOIS C,ZHENG A X,et al. Collaborative denoising auto-encoders for top-n recommender systems[C]//Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. San Francisco,USA,2016.
[65]DENG S,HUANG L,XU G,et al. On deep learning for trust-aware recommendations in social networks[J]. IEEE Transactions on neural networks and learning systems,2017,28(5):1164-1177.
[66]HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al. Session-based recommendations with recurrent neural networks[C]//International Conference on Learning Representations. San Juan,Puerto Rico,2016.
[67]TAN Y K,XU X,LIU Y. Improved recurrent neural networks for session-based recommendations[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Boston,USA,2016.
[68]KO Y J,MAYSTRE L,GROSSGLAUSER M. Collaborative recurrent neural networks for dynamic recommender systems[C]//Proceedings of the 8th Asian conference on Machine Learning. Hamiton,New Zealand,2016.
[69]DEVOOGHT R,BERSINI H. Collaborative filtering with recurrent neural networks[DB/OL]. [2018-03-06]. https://arxiv.org/abs/1608.07400.
[70]WU S,REN W,YU C,et al. Personal recommendation using deep recurrent neural networks in NetEase[C]//IEEE International Conference on Data Engineering. Paris,France,2016.
[71]LEE H,AHN Y,LEE H,et al. Quote recommendation in dialogue using deep neural network[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. Pisa,Italy,2016.
[72]SONG Y,ELKAHKY A M,HE X. Multi-rate deep learning for temporal recommendation[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. Pisa,Italy,2016.
[73]HUANG P S,HE X,GAO J,et al. Learning deep structured semantic models for web search using clickthrough data[C]//ACM International Conference on Information and Knowledge Management. San Francisco,USA,2013.
[74]DAI H,WANG Y,TRIVEDI R,et al. Recurrent coevolutionary feature embedding processes for recommendation[DB/OL]. [2018-03-06]. https://arxiv.org/abs/1609.03675.
[75]DAI H,WANG Y,TRIVEDI R,et al. Recurrent coevolutionary latent feature processes for continuous-time recommendation[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Boston,USA,2016.
[76]ZUO Y,ZENG J,GONG M,et al. Tag-aware recommender systems based on deep neural networks[J]. Neurocomputing,2016,204:51-60.
[77]XU Z,CHEN C,LUKASIEWICZ T,et al. Tag-aware personalized recommendation using a deep-semantic similarity model with negative sampling[C]//Proceedings of the 25th ACM International Conference on Information and Knowledge Management. Singapore,2016.
[78]WANG H,SHI X,YEUNG D Y. Relational stacked denoising autoencoder for tag recommendation[C]//Proceedings of the 29th AAAI Conference on Artifical Intelligence. Austin,USA,2015.
[79]GUPTA A K,NAGAR D K. Matrix variate normal distribution[M]. Boca Raton:CRC Press,1999.
[80]HAMEL P,LEMIEUX S,BENGIO Y,et al. Temporal pooling and multiscale learning for automatic annotation and ranking of music audio[C]//International Society for Music Information Retrieval Conference. Miami,USA,2011.
[81]RAWAT Y S,KANKANHALLI M S. ConTagNet:exploiting user context for image tag recommendation[C]//Proceedings of the 2016 ACM on Multimedia Conference. Amsterdam,Netherlands,2016.

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Last Update: 2018-12-30