[
[1] Hidasi B, Karatzoglou A, Baltrunas L, and Tikk D (2015) Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939.
]Search in Google Scholar
[
[2] Quadrana M, Karatzoglou A, Hidasi B, and Cremonesi P (2017) Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 130137.10.1145/3109859.3109896
]Search in Google Scholar
[
[3] Tang J and Wang K (2018) Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. In ACM International Conference on Web Search and Data Mining.10.1145/3159652.3159656
]Search in Google Scholar
[
[4] Tuan T X and Phuong T M (2017) 3D Convolutional Networks for Session-based Recommendation with Content Features. In RecSys. ACM.10.1145/3109859.3109900
]Search in Google Scholar
[
[5] Ma C, Kang P, and Liu X (2019) Hierarchical Gating Networks for Sequential Recommendation. arXiv preprint arXiv:1906.09217.10.1145/3292500.3330984
]Search in Google Scholar
[
[6] Tang J, Belletti F, Jain S, Chen M, Beutel A, Xu C, and Chi E H (2019) Towards neural mixture recommender for long range dependent user sequences. In The World Wide Web Conference. ACM, 17821793.10.1145/3308558.3313650
]Search in Google Scholar
[
[7] Yuan F, Karatzoglou A, Arapakis I, Jose J M, and He X (2019) A Simple Convolutional Generative Network for Next Item Recommendation. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM, 582590.10.1145/3289600.3290975
]Search in Google Scholar
[
[8] Kang W-C and McAuley J (2018) Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 197206.
]Search in Google Scholar
[
[9] Fang H, Guo G, Zhang D, and Shu Y (2019) Deep Learning-Based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations. In International Conference on Web Engineering. Springer, 574577.10.1007/978-3-030-19274-7_47
]Search in Google Scholar
[
[10] Li J, Ren P, Chen Z, Ren Z, Lian T, and Ma J (2017) Neural Attentive Session-based Recommendation. In CIKM. ACM, 14191428.10.1145/3132847.3132926
]Search in Google Scholar
[
[11] Tan Y K, Xu X, and Liu Y (2016) Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 1722.10.1145/2988450.2988452
]Search in Google Scholar
[
[12] Kalchbrenner N, van den Oord A, Simonyan K, Danihelka I, Vinyals O, Graves A, and Kavukcuoglu K (2017) Video pixel networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 17711779.
]Search in Google Scholar
[
[13] Van den Oord A, Kalchbrenner N, Espeholt L, Vinyals O, Graves A, et al. (2016) Conditional image generation with pixelcnn decoders. In Advances in neural information processing systems. 47904798.
]Search in Google Scholar
[
[14] Bahdanau D, Cho K, and Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
]Search in Google Scholar
[
[15] Kalchbrenner N, Espeholt L, Simonyan K, van den Oord A, Graves A, and Kavukcuoglu K (2016) Neural machine translation in linear time. arXiv preprint arXiv:1610.10099.
]Search in Google Scholar
[
[16] Sutskever I, Vinyals O, and Le Q V (2014) Sequence to sequence learning with neural networks. In Advances in neural information processing systems. 31043112.
]Search in Google Scholar
[
[17] Sakaguchi K, Arase Y, and Komachi M (2013) Discriminative approach to fillin-the-blank quiz generation for language learners. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vol. 2. 238242.
]Search in Google Scholar
[
[18] Devlin J, Chang M-W, Lee E, and Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
]Search in Google Scholar
[
[19] Wang S, Cao L, and Wang Y (2019) A Survey on Session-based Recommender Systems. arXiv preprint arXiv:1902.04864.
]Search in Google Scholar
[
[20] Hidasi B and Karatzoglou A (2017) Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. arXiv preprint arXiv:1706.03847.
]Search in Google Scholar
[
[21] Gu Y, Lei T, Barzilay R, and Jaakkola T S (2016) Learning to refine text based recommendations. In EMNLP. 21032108.10.18653/v1/D16-1227
]Search in Google Scholar
[
[22] Smirnova E and Vasile F (2017) Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks. arXiv preprint arXiv:1706.07684.10.1145/3125486.3125488
]Search in Google Scholar
[
[23] Yan A, Cheng S, Kang W-C, Wan M, and McAuley J (2019) CosRec: 2D Convolutional Neural Networks for Sequential Recommendation. arXiv preprint arXiv:1908.09972.10.1145/3357384.3358113
]Search in Google Scholar
[
[24] You J, Wang Y, Pal A, Eksombatchai P, Rosenburg C, and Leskovec J (2019) Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems. In The World Wide Web Conference. ACM, 22362246.10.1145/3308558.3313747
]Search in Google Scholar
[
[25] Pandey P, Kumar S, Das S, Craciun E-M (2019) Numerical solution of two dimensional reaction-diffusion equation using operational matrix method based on Genocchi polynomial. Part I: Genocchi polynomial and operational matrix. In Proceedings of the Romanian Academy Series A, 20(4), 393399.
]Search in Google Scholar
[
[26] Bobe A, Nicola A, Popa C (2015) Weaker hypotheses for the general projection algorithm with corrections. An. St. Univ.” Ovidius” Constanta-Seria Matematica 23(3), 9-16.10.1515/auom-2015-0043
]Search in Google Scholar
[
[27] Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, and Jiang P (2019) BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 14411450.10.1145/3357384.3357895
]Search in Google Scholar
[
[28] Zhang S, Tay Y, Yao L, and Sun A (2018) Next item recommendation with self-attention. arXiv preprint arXiv:1808.06414.
]Search in Google Scholar
[
[29] Peters M E, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, and Zettle-moyer L (2018) Deep contextualized word representations. arXiv preprint arXiv:1802.05365.10.18653/v1/N18-1202
]Search in Google Scholar
[
[30] Song K, Tan X, Qin T, Lu J, and Liu T-Y (2019) MASS: Masked Sequence to Sequence Pre-training for Language Generation. arXiv preprint arXiv:1905.02450.
]Search in Google Scholar
[
[31] Yuan F, Guo G, Jose J M, Chen L, Yu H, and Zhang W (2016) Lambdafm: learning optimal ranking with factorization machines using lambda surrogates. In CIKM. ACM, 227236.10.1145/2983323.2983758
]Search in Google Scholar
[
[32] Yuan F, Xin X, He X, Guo G, Zhang W, Tat-Seng C, and Jose J M (2018) fBGD: Learning embeddings from positive unlabeled data with BGD.
]Search in Google Scholar
[
[33] Rendle S and Freudenthaler C (2014) Improving pairwise learning for item recommendation from implicit feedback. In Proceedings of the 7th ACM international conference on Web search and data mining. ACM, 273282.10.1145/2556195.2556248
]Search in Google Scholar
[
[34] Wang J, Yu L, Zhang W, Gong Y, Xu Y, Wang B, Zhang P, and Zhang D (2017) Irgan: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 515524.10.1145/3077136.3080786
]Search in Google Scholar
[
[35] Jean S, Cho K, Memisevic R, and Bengio Y (2014) On using very large target vocabulary for neural machine translation. arXiv preprint arXiv:1412.2007.
]Search in Google Scholar
[
[36] Kingma D and Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
]Search in Google Scholar
[
[37] Meng W, Yang D, and Xiao Y (2020) Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation. In Proceedings of the 43rd International SIGIR Conference on Research and Development Information Retrieval. 110.10.1145/3397271.3401098
]Search in Google Scholar
[
[38] Qiu R, Huang Z, Li J, and Yin H (2020) Exploiting cross-session information for session-based recommendation with graph neural networks. ACM Transactions on Information Systems 38 (2020), 123. Issue 3.10.1145/3382764
]Search in Google Scholar
