Computer Science ›› 2019, Vol. 46 ›› Issue (8): 111-115.doi: 10.11896/j.issn.1002-137X.2019.08.018

• HPC China 2018 • Previous Articles     Next Articles

Deep Neural Network Recommendation Model Based on User Vectorization Representation and Attention Mechanism

GUO Xu1, ZHU Jing-hua1,2   

  1. (School of Computer Science and Technology,Heilongjiang University,Harbin 150080,China)1
    (Key Laboratory of Database and Parallel Computing of Heilongjiang Province,Harbin 150080,China)2
  • Received:2018-10-21 Online:2019-08-15 Published:2019-08-15

Abstract: With the rapid development of Internet application,recommendation system,as an effective measure to solve information overloading,has become a research hot topic in industry and academia.Traditional recommendation algorithms for users’ implicit feedback are mainly based on collaborative filtering and learning-to-rank method,which do not make full use of the implicit feedback features in users’ behaviors.In this paper,a users’ vectorization representation model based on neural network was proposed,which can make full use of heterogeneous implicit feedback features of users’ behaviors.At the same time,referring to the self-attention mechanism in machine translation,this paper designed a neural attentive recommendation model which integrates the dynamic temporal features of user-item interaction and user vectorization representation,to improve the performance of the recommendation system.The comparison experiment is conducted on two public datasets,and the recommendation performance is evaluated by recall,precision and NDCG.Compared with other recommendation models for implicit feedback,the proposed recommendation model has better recommendation performance and better generalization ability

Key words: Recommendation system, Neural networks, Attention mechanism, Implicit feedback, Deep learning

CLC Number: 

  • TP391
[1] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[M]∥Advances in Neural Information Processing Systems.Bertin:Springer,2017:5998-6008.
[2] SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥Procee-dings of the 10th International Conference on World Wide Web.ACM,2001:285-295.
[3] OSTUNI V C,DI NOIA T,DI SCIASCIO E,et al.Top-n recommendations from implicit feedback leveraging linked open data[C]∥Proceedings of the 7th ACM Conference on Recommender Systems.ACM,2013:85-92.
[4] ZIMDARS A,CHICKERING D M,MEEK C.Using temporal data for making recommendations[C]∥Proceedings of the Se-venteenth Conference on Uncertainty in artificial intelligence.Morgan Kaufmann Publishers Inc.,2001:580-588.
[5] RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]∥Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence.AUAI Press,2009:452-461.
[6] 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.
[7] 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.International World Wide Web Confe-rences Steering Committee,2015:278-288.
[8] HE X,LIAO L,ZHANG H,et al.Neural collaborative filtering[C]∥Proceedings of the 26th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee,2017:173-182.
[9] HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al.Session-based recommendations with recurrent neural networks[J].arXiv:1511.06939,2015.
[10] GONG Y,ZHANG Q.Hashtag Recommendation Using Attention-Based Convolutional Neural Network[C]∥Proceedings of International Joint Conference on Artificial Intelligence.AAAI Press,2016:2782-2788.
[11] LI Y,LIU T,JIANG J,et al.Hashtag recommendation with topical attention-based LSTM[C]∥Proceedings of the 26th International Conference on Computational Linguistics.ACL,2016:3019-3029.
[12] XIAO J,YE H,HE X,et al.Attentional factorization machines:Learning the weight of feature interactions via attention networks[J].arXiv:1708.04617,2017.
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