Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 493-496.

• Big Data & Data Mining • Previous Articles     Next Articles

Research on Recommendation Application Based on Seq2seq Model

CHEN Jun-hang, XU Xiao-ping, YANG Heng-hong   

  1. School of Electronics and Information,Guangdong Polytechnic Normal University,Guangzhou 510000,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: There is enormous information around us in daily basis which lead to the recommander systems to filter out the pure gold.The traditional recommander systems have been regarded as static,and lack of the research about the long or short term dependency of data.Considering the outstanding perform of recurrent neural network in tackling the sequence data,recommander system based on seq2seq model was built.The process of recommandation can be viewed as a process of sequence translation or a process of answer generation,and the model make uses of the used interactive sequence data to learn the inherent frequent patterns,then makes the prediction of other users’ actions with items.Two datasets usually used for recommender system test are involved in the experiments,which measured by the BLEU.The results show that the method can make the sequence recommendation.The model only needs the interactive data between users and items,and gets rid of the rating matrix,thus avoids the sparsity problem.

Key words: Recommender system, Recurrent neural networkm, Seq2seq model

CLC Number: 

  • TP391.4
[1]PESKA L,VOJTAS P.Using implicit preference relations to improve recommender systems[J].Journal on Data Semantics,2017,6(1):15-30.
[2]ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J].IEEE transactions on knowledge and data engineering,2005,17(6):734-749.
[3]SU X,KHOSHGOFTAAR T M.A survey of collaborative filtering techniques[J].Advances in artificial intelligence,2009,2009:4.
[4]ZHANG S,YAO L,SUN A.Deep learning based recommender system:A survey and new perspectives[J].arXiv preprint arXiv:1707.07435,2017.
[5]MIYAHARA K,PAZZANI M J.Improvement of collaborative filtering with the simple Bayesian classifier[J].Information Processing Society of Japan,2002,43(11):679-689.
[6]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].IEEE Computer,2009,42(8):30-37.
[7]KOREN Y.Collaborative filtering with temporal dynamics[J].Communications of the ACM,2010,53(4):89-97.
[8]SHANI G,HECKERMAN D,BRAFMAN R I.An MDP-based recommender system[J].Journal of Machine Learning Research,2005,6(1):1265-1295.
[9]HE X,LIAO L,ZHANG H,et al.Neural collaborative filtering[C]∥Proceedings of the 26th International Conference on World Wide Web.2017:173-182.
[10]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.ACM,2015:111-112.
[11]GONG Y,ZHANG Q.Hashtag Recommendation Using Attention-Based Convolutional Neural Network[C]∥International Joint Conference on Artifical Intelligence.2016:2782-2788.
[12]FISCHER A,IGEL C.An introduction to restricted Boltzmann machines[C]∥Iberoamerican Congress on Pattern Recognition.2012:14-36.
[13]WU C Y,AHMED A,BEUTEL A,et al.Recurrent recommender networks[C]∥Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.ACM,2017:495-503.
[14]MUSTO C,GRECO C,SUGLIA A,et al.Ask Me Any Rating:A Content-based Recommender System based on Recurrent Neural Networks[OL].http://ceur-ws.org/vol-1653/paper_11.pdf.
[15]KO Y J,MAYSTRE L,GROSSGLAUSER M.Collaborative recurrent neural networks for dynamic recommender systems[C]∥Asian Conference on Machine Learning.2016:366-381.
[16]HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al.Session-based recommendations with recurrent neural networks[J].arXiv preprint arXiv:1511.06939,2015.
[17]SUTSKEVER I,MARTENS J,HINTON G E.Generating text with recurrent neural networks[C]∥Proceedings of the 28th International Conference on Machine Learning (ICML-11).2011:1017-1024.
[18]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]∥Advances in neural information processing systems.2013:3111-3119.
[19]DEVOOGHT R,BERSINI H.Collaborative filtering with recurrent neural networks[J].arXiv preprint arXiv:1608.07400,2016.
[20]DEVOOGHT R,BERSINI H.Long and Short-Term Recom-mendations with Recurrent Neural Networks[C]∥Proceedings of the 25th Conference on User Modeling,Adaptation and Personalization.ACM,2017:13-21.
[21]SUTSKEVER I,VINYALS O,LE Q V.Sequence to sequence learning with neural networks[C]∥Advances in neural information processing systems.2014:3104-3112.
[22]CHO K,VAN B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J].arXiv preprint arXiv:1406.1078,2014.
[23]LUONG M T,PHAM H,MANNING C D.Effective approaches to attention-based neural machine translation[J].arXiv preprint arXiv:1508.04025,2015.
[24]BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv preprint arXiv:1409.0473,2014.
[25]ZAREMBA W,SUTSKEVER I,VINYALS O.Recurrent neural network regularization[J].arXiv preprint arXiv:1409.2329,2014.
[26]JEAN S,CHO K,MEMISEVIC R,et al.On using very large target vocabulary for neural machine translation[J].arXiv preprint arXiv:1412.2007,2014.
[27]Vinyals O,Le Q.A neural conversational model[J].arXiv preprint arXiv:1506.05869,2015.
[28]PAPINENI K,ROUKOS S,WARD T,et al.BLEU:a method for automatic evaluation of machine translation[C]∥Procee-dings of the 40th Annual Meeting on Association for Computational Linguistics.Association for Computational Linguistics,2002:311-318.
[29]CHUNG J,GULCEHRE C,CHO K H,et al.Empirical evaluation of gated recurrent neural networks on sequence modeling[J].arXiv preprint arXiv:1412.3555,2014.
[30]LIU S J,YANG N,LI M,et al.A recursive recurrent neural network for statistical machine translation[C]∥Meeting of the Associaton for Computational Linguistics.2014:1491-1500.
[31]CHO K,BAHDANAU D,et al.On the properties of neural machine translation:Encoder-decoder approaches[J].arXiv preprint arXiv:1409.1259,2014.
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