Computer Science ›› 2020, Vol. 47 ›› Issue (3): 103-109.doi: 10.11896/jsjkx.190500183

• Database & Big Data & Data Science • Previous Articles     Next Articles

Session-based Recommendation Algorithm Based on Recurrent Temporal Convolutional Network

LI Tai-song1,2,HE Ze-yu1,2,WANG Bing1,2,YAN Yong-hong1,2,3,TANG Xiang-hong4   

  1. (Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China)1;
    (School of Electronic, Electrical and Communication Engineering, University of Chinese Academy Sciences, Beijing 100190, China)2;
    (Xinjiang Key Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumchi 830011, China)3;
    (Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, China)4
  • Received:2019-05-31 Online:2020-03-15 Published:2020-03-30
  • About author:LI Tai-song,born in 1990,doctorial student.His main research interests include recommendation system and data mining. WANG Bing,born in 1984,Ph.D,asso-ciate professor.Her main research interests include recommendation system and data mining.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (11590770-4, 11722437, 61650202, U1536117, 61671442, 11674352, 11504406, 61601453), National Key Research and Development Program (2016YFB0801203, 2016YFC0800503, 2017YFB1002803), Key Science and Technology Project of the Xinjiang Uygur Autonomous Region (2016A03007-1) and Project of Guizhou High-level Study Abroad Talents Innovation and Entrepreneurship (2018.0002).

Abstract: Since the Recurrent Neural Network (RNN) generally models transition patterns,ignores the inner connection of items and can’t model the long-term evolving patterns of sequential data in session-based recommendations.A Recurrent Temporal Convolutional Network (RTCN) was proposed.Firstly,each item in the sequence is embedded as a vector,the multi-layer casual convolutions and dilated convolutions are applied so that the receptive field is improved and the long-term connections are established.A residual network is stacked to extract features from different layers.Therefore,the gradient vanishing or even disappearing in back propagation can be solved.With above operations,a well-designed Temporal Convolutional Network (TCN) is established.It extracts local features from sequence items,maps item information into latent space and generates fine-grained feature vectors as results.To further explore the connections between items in macroscopic way,the feature vectors are feed into Gated Recurrent Unit (GRU).After multiple iterations and updates to hidden states,the model can make a prediction of the next item.RTCN can extract long-time,multi-dimension,fine-grained local features from inputs by adapting temporal convolutional network.It also models the long-distance connections between items,captures the transition patterns and infers the next items by using GRU networks.The experimental results demonstrate that the RTCN model outperforms 6%~13% than RNN-based model and 9%~59% than other traditional recommendation methods under the metrics of Recall and Mean Reciprocal Rank (MRR).By comparing different definitions of loss,RTCN performs best under the cross entropy loss function.Meanwhile,due to the TCN multi-channel structure,the proposed model has a high potential capacity to embedding context features of items and users when the dataset information is rich.

Key words: Deep learning, Recommendation system, Recurrent neural network, Session-based recommendation, Temporal convolutional network

CLC Number: 

  • TP183
[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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