2019

Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation

Zhang, Mengqi, Wu, Shu, Gao, Meng et al.

Understand

The problem of session-aware recommendation aims to predict users' next click based on their current session and historical sessions.

Existing session-aware recommendation methods have defects in capturing complex item transition relationships.
Other than that, most of them fail to explicitly distinguish the effects of different historical sessions on the current session.
To this end, we propose a novel method, named Personalized Graph Neural Networks with Attention Mechanism (A-PGNN) for brevity.

Built on

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th International Conference on World Wide Web , ser. WWW ’01, 2001
2001
Earlier work this paper cites.
G. Shani, R. I. Brafman, and D. Heckerman, “An mdp-based recommender system,” in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence , ser. UAI’02. Morgan Kaufmann Publishers Inc., 2002, pp. 453–460
2002
Earlier work this paper cites.
M. Gori, G. Monfardini, and F. Scarselli, “A new model for learning in graph domains,” in Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. , vol. 2, July 2005, pp. 729–734 vol. 2
2005
Earlier work this paper cites.
A. Mnih and R. Salakhutdinov, “Probabilistic matrix factorization,” in Advances in neural information processing systems , 2007, pp. 1257–1264
2007
Earlier work this paper cites.
Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer , vol. 42, no. 8, pp. 30–37, 2009
2009
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Transactions on Neural Networks , vol. 20, no. 1, pp. 61–80, Jan 2009
2009
Earlier work this paper cites.
S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, “Factorizing personalized markov chains for next-basket recommendation,” in Proceedings of the 19th international conference on World wide web . ACM, 2010, pp. 811–820
2010
Earlier work this paper cites.
T. Mikolov, M. Karafiát, L. Burget, J. Cernockỳ, and S. Khudanpur, “Recurrent neural network based language model,” in INTERSPEECH , vol. 2, 2010, p. 3
2010
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics , 2010, pp. 249–256
2010
Earlier work this paper cites.
Y. Koren and R. Bell, “Advances in collaborative filtering,” in Recommender Systems Handbook . Springer, 2011, pp. 145–186
2011
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Annual Conference on Neural Information Processing Systems , 2013, pp. 3111–3119
2013
Earlier work this paper cites.
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” In Proceedings of the Conference on Empirical Methods in Natural Language Processing , pp. 1724–1734, 2014
2014
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD ’14. ACM, 2014, pp. 701–710
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” Computer Science , 2014
2014
Earlier work this paper cites.
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in Proceedings of the 24th International Conference on World Wide Web , ser. WWW ’15. International World Wide Web Conferences Steering Committee, 2015, pp. 1067–1077
2015
Earlier work this paper cites.
D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 , ser. NIPS’15, 2015
2015
Earlier work this paper cites.

Similar

Y. Li, D. Tarlow, M. Brockschmidt, and R. S. Zemel, “Gated graph sequence neural networks,” in Proceedings of the 2015 International Conference on Learning Representations , ser. ICLR ’15, 2015
2015
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 , 2015
Original
2015
Cited alongside, same era.
B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, “Session-based recommendations with recurrent neural networks,” in Proceedings of the 2016 International Conference on Learning Representations , ser. ICLR ’16, 2016
2016
Cited alongside, same era.
Y. K. Tan, X. Xu, and Y. Liu, “Improved recurrent neural networks for session-based recommendations,” in Proceedings of the 1st Workshop on Deep Learning for Recommender Systems , 2016, pp. 17–22
2016
Cited alongside, same era.
B. Twardowski, “Modelling contextual information in session-aware recommender systems with neural networks,” in Proceedings of the 10th ACM Conference on Recommender Systems , 2016, pp. 273–276
2016
Cited alongside, same era.
I. V. Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau, “Building end-to-end dialogue systems using generative hierarchical neural network models,” in Proceedings of the 30th AAAI Conference on Artificial Intelligence , 2016, pp. 3776–3784
2016
Cited alongside, same era.
B. Hidasi, M. Quadrana, A. Karatzoglou, and D. Tikk, “Parallel recurrent neural network architectures for feature-rich session-based recommendations,” in Proceedings of the 10th ACM Conference on Recommender Systems , ser. RecSys ’16. ACM, 2016, pp. 241–248
2016
Cited alongside, same era.
A. Grover and J. Leskovec, “Node2vec: Scalable feature learning for networks,” in Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD ’16. ACM, 2016, pp. 855–864
2016
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proceedings of the 2016 International Conference on Learning Representations , ser. ICLR ’16, 2016
2016
Cited alongside, same era.
C. Wu and M. Yan, “Session-aware information embedding for e-commerce product recommendation,” in Proceedings of the 2017 ACM on conference on information and knowledge management , 2017, pp. 2379–2382
2017
Cited alongside, same era.
V. W. Anelli, V. Bellini, T. Di Noia, W. La Bruna, P. Tomeo, and E. Di Sciascio, “An analysis on time-and session-aware diversification in recommender systems,” in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization , 2017, pp. 270–274
2017
Cited alongside, same era.
M. Quadrana, A. Karatzoglou, B. Hidasi, and P. Cremonesi, “Personalizing session-based recommendations with hierarchical recurrent neural networks,” in Proceedings of the Eleventh ACM Conference on Recommender Systems , 2017, pp. 130–137
2017
Cited alongside, same era.

Then

J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, and J. Ma, “Neural attentive session-based recommendation,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , ser. CIKM ’17. ACM, 2017, pp. 1419–1428
2017
Later among the works it cites.
C. Wu and M. Yan, “Session-aware information embedding for e-commerce product recommendation,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , ser. CIKM ’17. ACM, 2017, pp. 2379–2382
2017
Later among the works it cites.
R. Li, M. Tapaswi, R. Liao, J. Jia, R. Urtasun, and S. Fidler, “Situation recognition with graph neural networks,” in 2017 IEEE International Conference on Computer Vision (ICCV) , Oct 2017, pp. 4183–4192
2017
Later among the works it cites.
K. Marino, R. Salakhutdinov, and A. Gupta, “The more you know: Using knowledge graphs for image classification,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , vol. 00, July 2017, pp. 20–28
2017
Later among the works it cites.
D. Jannach and M. Ludewig, “When recurrent neural networks meet the neighborhood for session-based recommendation,” in Proceedings of the Eleventh ACM Conference on Recommender Systems , 2017, pp. 306–310
2017
Later among the works it cites.
B. Hidasi and A. Karatzoglou, “Recurrent neural networks with top-k gains for session-based recommendations,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management , 2018, pp. 843–852
2018
Later among the works it cites.
Q. Liu, Y. Zeng, R. Mokhosi, and H. Zhang, “Stamp: Short-term attention/memory priority model for session-based recommendation,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD ’18. ACM, 2018, pp. 1831–1839
2018
Later among the works it cites.
H. Ying, F. Zhuang, F. Zhang, Y. Liu, G. Xu, X. Xie, H. Xiong, and J. Wu, “Sequential recommender system based on hierarchical attention networks,” in the 27th International Joint Conference on Artificial Intelligence , 2018
2018
Later among the works it cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net, 2018
2018
Later among the works it cites.
Z. Li, X. Ding, and T. Liu, “Constructing narrative event evolutionary graph for script event prediction,” 05 2018
2018
Later among the works it cites.
M. Ludewig and D. Jannach, “Evaluation of session-based recommendation algorithms,” User Modeling and User-Adapted Interaction , vol. 28, no. 4-5, pp. 331–390, 2018
2018
Later among the works it cites.
S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, “Session-based recommendation with graph neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 346–353
2019
Closest in time.
J. You, Y. Wang, A. Pal, P. Eksombatchai, C. Rosenburg, and J. Leskovec, “Hierarchical temporal convolutional networks for dynamic recommender systems,” in The World Wide Web Conference . ACM, 2019, pp. 2236–2246
2019
Closest in time.
T. M. Phuong, T. C. Thanh, and N. X. Bach, “Neural session-aware recommendation,” IEEE Access , vol. 7, pp. 86 884–86 896, 2019
2019
Closest in time.
T. Liang, Y. Li, R. Li, X. Gu, and Y. Hu, “Personalizing session-based recommendation with dual attentive neural network,” in 2019 International Joint Conference on Neural Networks (IJCNN) , 2019
2019
Closest in time.
R. Qiu, J. Li, Z. Huang, and H. Yin, “Rethinking the item order in session-based recommendation with graph neural networks,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management , 2019, pp. 579–588
2019
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…