This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Huang Liwei, Jiang Bitao, Lu Shouye et al. Review of recommendation systems based on Deep Learning [J]. Journal of Computers, 2018, 41(07):1619–1647.LiweiHuangBitaoJiangShouyeLuReview of recommendation systems based on Deep Learning [J]Journal of Computers2018410716191647Search in Google Scholar
Beltramelli T. pix2code: Generating Code from a Graphical User Interface Screenshot [C]//Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems. 2018: 1–6.BeltramelliTpix2code: Generating Code from a Graphical User Interface Screenshot [C]Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems201816Search in Google Scholar
Ahmad W U, Chakraborty S, Ray B, et al. Unified Pre-training for Program Understanding and Generation [J]. 2021. DOI: 10.18653/v1/2021.naacl-main.211.AhmadW UChakrabortySRayBUnified Pre-training for Program Understanding and Generation [J]202110.18653/v1/2021.naacl-main.211Open DOISearch in Google Scholar
Wang Y, Wang W, Joty S, et al. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation [J]. 2021.WangYWangWJotySCodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation [J]2021Search in Google Scholar
Raffel C, Shazeer N, Roberts A, et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [J]. 2019. DOI: 10.48550/arXiv.1910.10683.RaffelCShazeerNRobertsAExploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [J]201910.48550/arXiv.1910.10683Open DOISearch in Google Scholar
Feng Yanxing, Research on Program Generation in AORBCO Model [D]. Xi'an University of Technology, 2021. DOI: 10.27391/dcnki.gxagu.2021.000121YanxingFengResearch on Program Generation in AORBCO Model [D]Xi'an University of Technology202110.27391/dcnki.gxagu.2021.000121Open DOISearch in Google Scholar
Xiao Liangshun, Research on Knowledge Fusion in AORBCO Modeling [D]. Xi'an University of Technology, 2023.LiangshunXiaoResearch on Knowledge Fusion in AORBCO Modeling [D]Xi'an University of Technology2023Search in Google Scholar
He X, Liao L, Zhang H, et al. Neural Collaborative Filtering [J]. International World Wide Web Conferences Steering Committee, 2017. DOI: 10.1145/3038912.3052569.HeXLiaoLZhangHNeural Collaborative Filtering [J]International World Wide Web Conferences Steering Committee201710.1145/3038912.3052569Open DOISearch in Google Scholar
Pennington J, Socher R, Manning C. Glove: Global Vectors for Word Representation [J]. 2014. DOI: 10.3115/v1/D14-1162.PenningtonJSocherRManningCGlove: Global Vectors for Word Representation [J]201410.3115/v1/D14-1162Open DOISearch in Google Scholar
Rasley J, Rajbhandari S, Ruwase O, et al. DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters [C]//KDD'20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2020. DOI: 10.1145/3394486.3406703.RasleyJRajbhandariSRuwaseODeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters [C]KDD'20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM202010.1145/3394486.3406703Open DOISearch in Google Scholar
Lewis P, Perez E, Piktus A, et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks [J]. 2020. DOI: 10.48550/arXiv.2005.11401.LewisPPerezEPiktusARetrieval-Augmented Generation for Knowledge-Intensive NLP Tasks [J]202010.48550/arXiv.2005.11401Open DOISearch in Google Scholar
Karpukhin V, Ouz B, Min S, et al. Dense Passage Retrieval for Open-Domain Question Answering [J]. 2020. DOI: 10.18653/v1/2020.emnlp-main.550KarpukhinVOuzBMinSDense Passage Retrieval for Open-Domain Question Answering [J]202010.18653/v1/2020.emnlp-main.550Open DOISearch in Google Scholar
Izacard G, Grave E. Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering [J]. 2020. DOI: 10.48550/arXiv.2007.01282.IzacardGGraveELeveraging Passage Retrieval with Generative Models for Open Domain Question Answering [J]202010.48550/arXiv.2007.01282Open DOISearch in Google Scholar
Wang H, Zhang F, Zhang M, et al. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems [J]. SIGKDD explorations, 2019.WangHZhangFZhangMKnowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems [J]SIGKDD explorations2019Search in Google Scholar
Li Xiang, Yang Xingyao, Yu Jiong et al. A bipartite recommendation algorithm based on knowledge graph convolutional networks [J]. Computer Science and Exploration, 2022, 16(01):176–184.XiangLiXingyaoYangJiongYuA bipartite recommendation algorithm based on knowledge graph convolutional networks [J]Computer Science and Exploration20221601176184Search in Google Scholar
Ren S, Guo D, Lu S, et al. CodeBLEU: a Method for Automatic Evaluation of Code Synthesis [J]. 2020. DOI: 10.48550/arXiv.2009.10297.RenSGuoDLuSCodeBLEU: a Method for Automatic Evaluation of Code Synthesis [J]202010.48550/arXiv.2009.10297Open DOISearch in Google Scholar
Barbella, Marcello and Tortora, Genoveffa, Rouge Metric Evaluation for Text Summarization Techniques. Available at SSRN: https://ssrn.com/abstract=4120317BarbellaMarcelloTortoraGenoveffaRouge Metric Evaluation for Text Summarization TechniquesAvailable at SSRN: https://ssrn.com/abstract=4120317Search in Google Scholar
Ehud Reiter; A Structured Review of the Validity of BLEU. Computational Linguistics 2018; 44 (3): 393–401. doi: https://doi.org/10.1162/coli_a_00322ReiterEhudA Structured Review of the Validity of BLEUComputational Linguistics2018443393401doi: https://doi.org/10.1162/coli_a_00322Search in Google Scholar