[1] GAI K, ZHU X Q, LI H, et al.Learning piece-wise linear models from large scale data for ad click prediction[EB/OL].[2021-01-20].https://arxiv.org/pdf/1704.05194.pdf. [2] HE X R, PAN J F, JIN O, et al.Practical lessons from predicting clicks on ads at facebook[C]//Proceedings of the 8th International Workshop on Data Mining for Online Advertising.New York, USA:ACM Press, 2014:1-9. [3] ZHOU G R, MOU N, FAN Y, et al.Deep interest evolution network for click-through rate prediction[EB/OL].[2021-01-20].https://arxiv.org/pdf/1809.03672.pdf. [4] ZHOU G R, ZHU X Q, SONG C R, et al.Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York, USA:ACM Press, 2018:1059-1068. [5] 周傲英, 周敏奇, 宫学庆.计算广告:以数据为核心的Web综合应用[J].计算机学报, 2011, 34(10):1805-1819. ZHOU A Y, ZHOU M Q, GONG X Q.Computational advertising:a data-centric comprehensive Web application[J].Chinese Journal of Computers, 2011, 34(10):1805-1819.(in Chinese) [6] STEFFEN R.Factorization machines[C]//Proceedings of the 10th IEEE International Conference on Data Mining.Washington D.C., USA:IEEE Press, 2010:995-1000. [7] SONG W P, SHI C C, XIAO Z P, et al.AutoInt:automatic feature interaction learning via self-attentive neural networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.New York, USA:ACM Press, 2019:1161-1170. [8] ZHANG W N, DU T M, WANG J.Deep learning over multi-field categorical data:a case study on user response prediction[EB/OL].[2021-01-20].https://arxiv.org/pdf/1601.02376.pdf. [9] QU Y R, CAI H, REN K, et al.Product-based neural networks for user response prediction[C]//Proceedings of the 16th IEEE International Conference on Data Mining.Washington D.C., USA:IEEE Press, 2016:1149-1154. [10] 项亮.推荐系统实战[M].北京:人民邮电出版社, 2012. XIANG L.Recommendation system practice[M].Beijing:Post and Telecom Press, 2012.(in Chinese) [11] PAN F Y, LI S K, AO X, et al.Warm up cold-start advertisements:improving CTR predictions via learning to learn ID embeeding[C]//Proceedings of the 42th International ACM SIGIR Conference on Research and Development in Information Retrieval.New York, USA:ACM Press, 2019:1-10. [12] JUAN Y C, ZHUANG Y, CHIN W S, et al.Field-aware factorization machines for CTR prediction[C]//Proceedings of the 10th ACM Conference on Recommender Systems.New York, USA:ACM Press, 2016:43-50. [13] HONG F X, HUANG D B, CHEN G.Interaction-aware factorization machines for recommender systems[C]//Proceedings of the 33th AAAI Conference on Artificial Intelligence.[S.l.]:AAAI Press, 2019:3804-3811. [14] PAN J W, XU J, RUIZ A L, et al.Field-weighted factorization machines for click-through rate prediction in display advertising[EB/OL].[2021-01-20].https://arxiv.org/pdf/1806.03514.pdf. [15] MATHIEU B, AKINORI F, NAONORI U, et al.Higher-order factorization machines[C]//Proceedings of the 30th International Conference on Neural Information Processing System.New York, USA:ACM Press, 2016:3351-3359. [16] XIAO J, HE X G, ZHANG H W, et al.Attentional factorization machines:learning the weight of feature interactions via attention networks[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.New York, USA:ACM Press, 2017:3119-3125. [17] HE X N, CHUA T S.Neural factorization machines for sparse predictive analytics[C]//Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval.New York, USA:ACM Press, 2017:335-364. [18] CHENG H T, LEVENT K, JEREMIAH H, et al.Wide & deep learning forrecommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.New York, USA:ACM Press, 2016:7-10. [19] GUO H F, TANG R M, YE Y M, et al.DeepFM:a factorization-machine based neural network for CTR prediction[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.New York, USA:ACM Press, 2017:1725-1731. [20] WANG R X, FU B, FU G, et al.Deep & cross network for ad click predictions[EB/OL].[2021-01-20].https://arxiv.org/pdf/1708.05123.pdf. [21] LIAN J X, ZHOU X H, ZHANG F Z, et al.xDeepFM:combining explicit and implicit feature interactions for recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York, USA:ACM Press, 2018:1754-1763. [22] LI Z Y, CHENG W, CHEN Y, et al.Interpretable click-through rate prediction through hierarchical attention[C]//Proceedings of the 13th ACM International Conference on Web Search and Data Mining.New York, USA:ACM Press, 2020:313-321. [23] CHENG W Y, SHEN Y Y, HUANG L P.Adaptive factorization network:learning adaptive-order feature interactions[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence.[S.l.]:AAAI Press, 2020:3609-3616. [24] HUANG T W, ZHANG Z Q, ZHANG J L.FiBiNET:combining feature importance and bilinear feature interaction for click-through rate prediction[C]//Proceedings of the 13th ACM Conference on Recommender Systems.New York, USA:ACM Press, 2019:169-177. [25] LIN B, TANG R M, CH EN Y Z, et al.Feature generation by convolutional neural network for click-through rate prediction[EB/OL].[2021-01-20].https://arxiv.org/pdf/1904.04447.pdf. [26] LI Z K, CUI Z Y, WU S, et al.Fi-GNN:modeling feature interactions via graph neural networks for CTR prediction[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.New York, USA:ACM Press, 2019:539-548. [27] LI Y J, DANIEL T, MARC B, et al.Gated graph sequence neural networks[EB/OL].[2021-01-20].https://arxiv.org/pdf/1511.05493.pdf. |