Computer Science ›› 2020, Vol. 47 ›› Issue (3): 103-109.doi: 10.11896/jsjkx.190500183
• Database & Big Data & Data Science • Previous Articles Next Articles
LI Tai-song1,2,HE Ze-yu1,2,WANG Bing1,2,YAN Yong-hong1,2,3,TANG Xiang-hong4
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[1]HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al.Session-based recommendations with recurrent neural networks[J].arXiv:1511.06939,2015. [2]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Nature,1986,323(6088):399-421. [3]KOREN Y,BELL R,VOLINSKY C.Matrix factorization tech- niques for recommender systems[J].Computer,2009,42(8):30-37. [4]WEIMER M,KARATZOGLOU A.Cofi rank-maximum margin matrix factorization for collaborative ranking[C]∥Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems.2008:1593-1600. [5]HIDASI B,TIKK D.Fast ALS-Based tensor factorization for context-aware recommendation from implicit feedback[C]∥Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Berlin:Springer,2012:67-82. [6]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥International Conference on World Wide Web.ACM,2001:285-295. [7]KOREN Y.Factorization meets theneighborhood:a multifaceted collaborative filtering model[C]∥ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2008:426-434. [8]HIDASI B,QUADRANA M,TIKK D.Parallel recurrent neural network architectures for feature-rich session-based recommendations[C]∥ACM Conference on Recommender Systems.ACM,2016:241-248. [9]BOGINA V,KUFLIK T.Incorporating dwell time in session- based recommendations with recurrent Neural networks [C]∥CEUR Workshop Proceedings.2017:57-59. [10]QUADRANA M,KARATZOGLOU A,HIDASI B,et al.Personalizing session-based recommendations with hierarchical recurrent neural networks[C]∥Eleventh ACM Conference on Recommender Systems.ACM,2017:130-137. [11]BAI S,KOLTER J Z,KOLTUN V.An empirical evaluation of generic convolutional and recurrent networks for sequence mo- deling [J].arXiv:1803.01271,2018. [12]LIANG M,HU X.Recurrent convolutional neural network for object recognition[C]∥Computer Vision and Pattern Recognition.IEEE,2015:3367-3375. [13]PINHEIRO P H O,COLLOBERT R.Recurrent convolutional neural networks for scene labeling[C]∥InternationalConfe-rence on International Conference on Machine Learning.2014:82-90. [14]LÉCUN Y,BOTTOU L,BENGIO Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [15]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer So-ciety,2015:3431-3440. [16]HE K,ZHANG X,REN S,et al.Deep residual learning for ima- ge recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2016:770-778. [17]SALIMANS T,KINGMA D P.Weight normalization:A simple reparameterization to accelerate training of deep neural networks[C]∥Advances in Neural Information Processing Systems.2016:901-909. [18]CHUNG J,GULCEHRE C,CHO K H,et al.Empirical evaluation of gated recurrent neural networks on sequence modeling[J].arXiv:1412.3555,2014. [19]BEN-SHIMON D,TSIKINOVSKY A,FRIEDMANN M,et al.Recsys challenge 2015 and the yoochoose dataset[C]∥RecSys’15:Proceedings of the 9th ACM Conference on Recommender Systems.New York:ACM,2015:357-358. [20]CHO E,MYERS S A,LESKOVEC J.Friendship and mobility:user movement in locationbased social networks[C]∥Procee-dings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2011:1082-1090. [21]CELMA O.Music Recommendation and Discovery in the Long Tail[M].Springer,2010. |
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