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Recommender systems is set up to address the issue of information overload in traditional information retrieval systems, which is focused on recommending information that is of most interest to users from massive information.
J. Ma, Z. Zhao, X. Yi, J. Chen, L. Hong, and E. H. Chi, “Modeling task relationships in multi-task learning with multi-gate mixture-of-experts,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD’18, 2018, pp. 1930–1939
1939
Earlier work this paper cites.
L. Guo, L. Hua, R. Jia, B. Zhao, X. Wang, and B. Cui, “Buying or browsing?: Predicting real-time purchasing intent using attention-based deep network with multiple behavior,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD’19, 2019, pp. 1984–1992
1992
Earlier work this paper cites.
Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE Transactions on Neural Networks , vol. 5, no. 2, pp. 157–166, 1994
1994
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering , vol. 17, no. 6, pp. 734–749, 2005
2005
Earlier work this paper cites.
A. Mnih and R. R. Salakhutdinov, “Probabilistic matrix factorization,” Advances in Neural Information Processing Systems , vol. 20, 2007
2007
Earlier work this paper cites.
H. Ma, H. Yang, M. R. Lyu, and I. King, “Sorec: Social recommendation using probabilistic matrix factorization,” in Proceedings of the 17th ACM Conference on Information and Knowledge Management , 2008, pp. 931–940
2008
Earlier work this paper cites.
R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proceedings of the 25th International Conference on Machine Learning , ser. ICML’08, 2008, pp. 160–167
2008
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, 2008
2008
Earlier work this paper cites.
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “Bpr: Bayesian personalized ranking from implicit feedback,” in Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence , ser. UAI’09, 2009, pp. 452–461
2009
Earlier work this paper cites.
Y. Koren, “Collaborative filtering with temporal dynamics,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD’09, 2009, pp. 447–456
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 , ser. WWW’10, 2010, pp. 811–820
2010
Earlier work this paper cites.
J. Liu, P. Dolan, and E. R. Pedersen, “Personalized news recommendation based on click behavior,” in Proceedings of the 15th International Conference on Intelligent User Interfaces , 2010, pp. 31–40
2010
Earlier work this paper cites.
N. N. Liu, E. W. Xiang, M. Zhao, and Q. Yang, “Unifying explicit and implicit feedback for collaborative filtering,” in Proceedings of the 19th ACM International Conference on Information and Knowledge Management , ser. CIKM’10, 2010, pp. 1445–1448
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, no. 3, 2010, pp. 1045–1048
2010
Earlier work this paper cites.
C. Desrosiers and G. Karypis, “A comprehensive survey of neighborhood-based recommendation methods,” Recommender Systems Handbook , pp. 107–144, 2011
2011
Earlier work this paper cites.
G. E. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 20, no. 1, pp. 30–42, 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems , vol. 25, 2012
2012
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing , ser. EMNLP, 2013, pp. 1631–1642
2013
Earlier work this paper cites.
Q. V. Le, “Building high-level features using large scale unsupervised learning,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing , ser. ICASSP, 2013, pp. 8595–8598
2013
Earlier work this paper cites.
L. Gai and L. Lei, “Dual collaborative topic modeling from implicit feedbacks,” in Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics , ser. SPAC, 2014, pp. 395–404
2014
Earlier work this paper cites.
C. C. Johnson, “Logistic matrix factorization for implicit feedback data,” Advances in Neural Information Processing Systems , vol. 27, no. 78, pp. 1–9, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
F. Ricci, L. Rokach, and B. Shapira, Recommender Systems Handbook (Second Edition) . Springer, 2015
2015
Earlier work this paper cites.
W. Pan, H. Zhong, C. Xu, and Z. Ming, “Adaptive bayesian personalized ranking for heterogeneous implicit feedbacks,” Knowledge-Based Systems , vol. 73, pp. 173–180, 2015
2015
Earlier work this paper cites.
C. A. Gomez-Uribe and N. Hunt, “The netflix recommender system: Algorithms, business value, and innovation,” ACM Transactions on Management Information Systems (TMIS) , vol. 6, no. 4, pp. 1–19, 2015
2015
Earlier work this paper cites.
Q. Liu, X. Zeng, C. Liu, H. Zhu, E. Chen, H. Xiong, and X. Xie, “Mining indecisiveness in customer behaviors,” in 2015 IEEE International Conference on Data Mining , ser. ICDM, 2015, pp. 281–290
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Earlier work this paper cites.
G. Li and W. Ou, “Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering,” Neurocomputing , vol. 204, pp. 17–25, 2016
2016
Earlier work this paper cites.
L. Tang, B. Long, B. Chen, and D. Agarwal, “An empirical study on recommendation with multiple types of feedback,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD’16, 2016, pp. 283–292
2016
Earlier work this paper cites.
W. Pan, M. Liu, and Z. Ming, “Transfer learning for heterogeneous one-class collaborative filtering,” IEEE Intelligent Systems , vol. 31, no. 4, pp. 43–49, 2016
2016
Earlier work this paper cites.
P. Covington, J. Adams, and E. Sargin, “Deep neural networks for youtube recommendations,” in Proceedings of the 10th ACM Conference on Recommender Systems , 2016, pp. 191–198
2016
Earlier work this paper cites.
B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, “Session-based recommendations with recurrent neural networks,” in Proceedings of the 4th International Conference on Learning Representations , ser. ICLR’16, 2016
2016
Earlier work this paper cites.
F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD’16, 2016, pp. 353–362
2016
Earlier work this paper cites.
B. Wu, T. Mei, W. Cheng, and Y. Zhang, “Unfolding temporal dynamics: Predicting social media popularity using multi-scale temporal decomposition,” in Proceedings of the 30th AAAI Conference on Artificial Intelligence , ser. AAAI’16, 2016, pp. 272–278
2016
Earlier work this paper cites.
X. He, H. Zhang, M. Kan, and T. Chua, “Fast matrix factorization for online recommendation with implicit feedback,” in Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR’16, 2016, pp. 549–558
2016
Earlier work this paper cites.
R. He and J. McAuley, “Fusing similarity models with markov chains for sparse sequential recommendation,” in Proceedings of the 16th IEEE International Conference on Data Mining , ser. ICDM’16, 2016, pp. 191–200
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT press, 2016
2016
Cited alongside, same era.
M. Welling and T. N. Kipf, “Semi-supervised classification with graph convolutional networks,” in Proceedings of the 5th International Conference on Learning Representation , ser. ICLR’17, 2016
2016
Cited alongside, same era.
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, “Gated graph sequence neural networks,” in Proceedings of the 4th International Conference on Learning Representations , ser. ICLR’16, 2016
2016
Cited alongside, same era.
W. Wang, W. Zhang, S. Liu, Q. Liu, B. Zhang, L. Lin, and H. Zha, “Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction,” in Proceedings of the Web Conference 2020 , ser. WWW’20, 2020, pp. 3056–3062
2020
Later among the works it cites.
Y. Gu, Z. Ding, S. Wang, and D. Yin, “Hierarchical user profiling for e-commerce recommender systems,” in Proceedings of the 13th International Conference on Web Search and Data Mining , ser. WSDM’20, 2020, pp. 223–231
2020
Later among the works it cites.
X. Chen, L. Lin, W. Pan, and Z. Ming, “A survey on heterogeneous one-class collaborative filtering,” ACM Transactions on Information Systems , vol. 38, no. 4, pp. 35:1–35:54, 2020
2020
Later among the works it cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 1, pp. 4–24, 2020
2020
Later among the works it cites.
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2017
Cited alongside, same era.
B. Smith and G. Linden, “Two decades of recommender systems at amazon.com,” IEEE Internet Computing , vol. 21, no. 3, pp. 12–18, 2017
2017
Cited alongside, same era.
R. Mehta and K. Rana, “A review on matrix factorization techniques in recommender systems,” in 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) , 2017, pp. 269–274
2017
Cited alongside, same era.
R. He, W. Kang, and J. McAuley, “Translation-based recommendation,” in Proceedings of the 11th ACM Conference on Recommender Systems , ser. RecSys’17, 2017, pp. 161–169
2017
Cited alongside, same era.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31th International Conference on Neural Information Processing Systems , ser. NeurIPS’17, 2017, pp. 1025–1035
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Cited alongside, same era.
G. Guo, H. Qiu, Z. Tan, Y. Liu, J. Ma, and X. Wang, “Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems,” Knowledge-Based Systems , vol. 138, pp. 202–207, 2017
2017
Cited alongside, same era.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics , 2017, pp. 1273–1282
2017
Cited alongside, same era.
2020
Later among the works it cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European conference on computer vision , 2020, pp. 213–229
2020
Later among the works it cites.
R. Xie, C. Ling, Y. Wang, R. Wang, F. Xia, and L. Lin, “Deep feedback network for recommendation,” in Proceedings of the 29th International Joint Conference on Artificial Intelligence , ser. IJCAI’20, 2020, pp. 2519–2525
2020
Later among the works it cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, P. J. Liu et al. , “Exploring the limits of transfer learning with a unified text-to-text transformer,” Jouranl of Machine Learning Research , vol. 21, no. 140, pp. 1–67, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
L. Floridi and M. Chiriatti, “Gpt-3: Its nature, scope, limits, and consequences,” Minds and Machines , vol. 30, pp. 681–694, 2020
2020
Later among the works it cites.
Y. Xu, Y. Zhu, and J. Yu, “Modeling multiple coexisting category-level intentions for next item recommendation,” ACM Transactions on Information Systems , vol. 39, no. 3, pp. 23:1–23:24, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Han, Y. Ma, Q. Mei, and X. Liu, “Deeprec: On-device deep learning for privacy-preserving sequential recommendation in mobile commerce,” in Proceedings of The Web Conference 2021 , ser. WWW’21, 2021, pp. 900–911
2021
Later among the works it cites.
F. Xiao, L. Li, W. Xu, J. Zhao, X. Yang, J. Lang, and H. Wang, “Dmbgn: Deep multi-behavior graph networks for voucher redemption rate prediction,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD’21, 2021, pp. 3786–3794
2021
Later among the works it cites.
W. Pan and K. Yang, “Multi-behavior graph neural networks for session-based recommendation,” in 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence , ser. MLBDBI, 2021, pp. 756–761
2021
Later among the works it cites.
Z. Bian, S. Zhou, H. Fu, Q. Yang, Z. Sun, J. Tang, G. Liu, K. Liu, and X. Li, “Denoising user-aware memory network for recommendation,” in Proceedings of the 15th ACM Conference on Recommender Systems , ser. RecSys’21, 2021, pp. 400–410
2021
Later among the works it cites.
L. Xia, C. Huang, Y. Xu, P. Dai, X. Zhang, H. Yang, J. Pei, and L. Bo, “Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence , ser. AAAI’21, 2021, pp. 4486–4493
2021
Later among the works it cites.
Z. Zhan, M. He, W. Pan, and Z. Ming, “Transrec++: Translation-based sequential recommendation with heterogeneous feedback,” Frontiers of Computer Science , vol. 16, no. 2, p. 162615, 2022
2022
Later among the works it cites.
C. Wu, F. Wu, L. Lyu, Y. Huang, and X. Xie, “Fedctr: Federated native ad ctr prediction with cross platform user behavior data,” ACM Transactions on Intelligent Systems and Technology , vol. 13, no. 4, pp. 62:1–62:19, 2022
2022
Later among the works it cites.
Y. Shen, B. Ou, and R. Li, “Mbn: Towards multi-behavior sequence modeling for next basket recommendation,” ACM Transactions on Knowledge Discovery from Data , vol. 16, no. 5, pp. 81:1–81:23, 2022
2022
Later among the works it cites.
W. Chen, M. He, Y. Ni, W. Pan, L. Chen, and Z. Ming, “Global and personalized graphs for heterogeneous sequential recommendation by learning behavior transitions and user intentions,” in Proceedings of the 16th ACM Conference on Recommender Systems , ser. RecSys’22, 2022, pp. 268–277
2022
Later among the works it cites.
J. Luo, M. He, W. Pan, and Z. Ming, “Bgnn: Behavior-aware graph neural network for heterogeneous session-based recommendation,” Frontiers of Computer Science , 2022
2022
Later among the works it cites.
Y. Liang, Q. Song, Z. Zhao, H. Zhou, and M. Gong, “Ba-gnn: Behavior-aware graph neural network for session-based recommendation,” Frontiers of Computer Science , vol. 0, no. 0, pp. 1–16, 2022
2022
Later among the works it cites.
M. He, J. Lin, J. Luo, W. Pan, and Z. Ming, “Flag: A feedback-aware local and global model for heterogeneous sequential recommendation,” ACM Transactions on Intelligent Systems and Technology , 2022
2022
Later among the works it cites.
J. Luo, M. He, X. Lin, W. Pan, and Z. Ming, “Dual-task learning for multi-behavior sequential recommendation,” in Proceedings of the 31st ACM International Conference on Information and Knowledge Management , ser. CIKM’22, 2022
2022
Later among the works it cites.
E. Yuan, W. Guo, Z. He, H. Guo, C. Liu, and R. Tang, “Multi-behavior sequential transformer recommender,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR’22, 2022, pp. 1642–1652
2022
Later among the works it cites.
M. He, W. Pan, and Z. Ming, “Bar: Behavior-aware recommendation for sequential heterogeneous one-class collaborative filtering,” Information Sciences , vol. 608, pp. 881–899, 2022
2022
Later among the works it cites.
Y. Yang, C. Huang, L. Xia, Y. Liang, Y. Yu, and C. Li, “Multi-behavior hypergraph-enhanced transformer for sequential recommendation,” in Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD’22, 2022, pp. 2263–2274
2022
Later among the works it cites.
L. Xia, C. Huang, Y. Xu, and J. Pei, “Multi-behavior sequential recommendation with temporal graph transformer,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
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2022
Later among the works it cites.
2022
Later among the works it cites.
2023
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2023
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2023
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C. Wu, F. Wu, T. Qi, Q. Liu, X. Tian, J. Li, W. He, Y. Huang, and X. Xie, “Feedrec: News feed recommendation with various user feedbacks,” in Proceedings of the ACM Web Conference 2022 , ser. WWW’22, 2022, pp. 2088–2097
2097
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