Literatura científica selecionada sobre o tema "Recommendation graph"
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Artigos de revistas sobre o assunto "Recommendation graph"
Jiang, Liwei, Guanghui Yan, Hao Luo e Wenwen Chang. "Improved Collaborative Recommendation Model: Integrating Knowledge Embedding and Graph Contrastive Learning". Electronics 12, n.º 20 (13 de outubro de 2023): 4238. http://dx.doi.org/10.3390/electronics12204238.
Texto completo da fonteChen, Fukun, Guisheng Yin, Yuxin Dong, Gesu Li e Weiqi Zhang. "KHGCN: Knowledge-Enhanced Recommendation with Hierarchical Graph Capsule Network". Entropy 25, n.º 4 (20 de abril de 2023): 697. http://dx.doi.org/10.3390/e25040697.
Texto completo da fonteTolety, Venkata Bhanu Prasad, e Evani Venkateswara Prasad. "Graph Neural Networks for E-Learning Recommendation Systems". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 9s (31 de agosto de 2023): 43–50. http://dx.doi.org/10.17762/ijritcc.v11i9s.7395.
Texto completo da fonteWang, Yan, Zhixuan Chu, Xin Ouyang, Simeng Wang, Hongyan Hao, Yue Shen, Jinjie Gu et al. "LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 17 (24 de março de 2024): 19189–96. http://dx.doi.org/10.1609/aaai.v38i17.29887.
Texto completo da fonteLiu, Jiawei, Haihan Gao, Chuan Shi, Hongtao Cheng e Qianlong Xie. "Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation". Applied Sciences 13, n.º 15 (1 de agosto de 2023): 8885. http://dx.doi.org/10.3390/app13158885.
Texto completo da fonteLi, Ran, Yuexin Li, Jingsheng Lei e Shengying Yang. "A Multi-Behavior Recommendation Method for Users Based on Graph Neural Networks". Applied Sciences 13, n.º 16 (16 de agosto de 2023): 9315. http://dx.doi.org/10.3390/app13169315.
Texto completo da fonteWu, Ziteng, Chengyun Song, Yunqing Chen e Lingxuan Li. "A review of recommendation system research based on bipartite graph". MATEC Web of Conferences 336 (2021): 05010. http://dx.doi.org/10.1051/matecconf/202133605010.
Texto completo da fonteYu, Wenhui, Zixin Zhang e Zheng Qin. "Low-Pass Graph Convolutional Network for Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junho de 2022): 8954–61. http://dx.doi.org/10.1609/aaai.v36i8.20878.
Texto completo da fonteZhang, Shengzhe, Liyi Chen, Chao Wang, Shuangli Li e Hui Xiong. "Temporal Graph Contrastive Learning for Sequential Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 8 (24 de março de 2024): 9359–67. http://dx.doi.org/10.1609/aaai.v38i8.28789.
Texto completo da fonteZeng, Yiping, e Shumin Liu. "Research on recommendation algorithm of Graph attention Network based on Knowledge graph". Journal of Physics: Conference Series 2113, n.º 1 (1 de novembro de 2021): 012085. http://dx.doi.org/10.1088/1742-6596/2113/1/012085.
Texto completo da fonteTeses / dissertações sobre o assunto "Recommendation graph"
Huang, Zan. "GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION". Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.
Texto completo da fonteLarsson, Carl-Johan. "Movie Recommendation System Using Large Scale Graph-Processing". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200601.
Texto completo da fonteSöderkvist, Nils. "Recommendation system for job coaches". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446792.
Texto completo da fonteOzturk, Gizem. "A Hybrid Veideo Recommendation System Based On A Graph Based Algorithm". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612624/index.pdf.
Texto completo da fonteLandia, Nikolas. "Content-awareness and graph-based ranking for tag recommendation in folksonomies". Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58069/.
Texto completo da fontePriya, Rashmi. "RETAIL DATA ANALYTICS USING GRAPH DATABASE". UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/67.
Texto completo da fonteOlmucci, Poddubnyy Oleksandr. "Graph Neural Networks for Recommender Systems". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25033/.
Texto completo da fonteBereczki, Márk. "Graph Neural Networks for Article Recommendation based on Implicit User Feedback and Content". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300092.
Texto completo da fonteRekommendationssystem används ofta på webbplatser och applikationer för att hjälpa användare att hitta relevant innehåll baserad på deras intressen. Med utvecklingen av grafneurala nätverk nådde toppmoderna resultat inom rekommendationssystem och representerade data i form av en graf. De flesta grafbaserade lösningar har dock svårt med beräkningskomplexitet eller att generalisera till nya användare. Därför föreslår vi ett nytt grafbaserat rekommendatorsystem genom att modifiera Simple Graph Convolution. De här tillvägagångssätt är en effektiv grafnodsklassificering och lägga till möjligheten att generalisera till nya användare. Vi bygger vårt föreslagna rekommendatorsystem för att rekommendera artiklarna från Peltarion Knowledge Center. Genom att integrera två datakällor, implicit användaråterkoppling baserad på sidvisningsdata samt innehållet i artiklar, föreslår vi en hybridrekommendatörslösning. Under våra experiment jämför vi vår föreslagna lösning med en matrisfaktoriseringsmetod samt en popularitetsbaserad och en slumpmässig baslinje, analyserar hyperparametrarna i vår modell och undersöker förmågan hos vår lösning att ge rekommendationer till nya användare som inte deltog av träningsdatamängden. Vår modell resulterar i något mindre men liknande Mean Average Precision och Mean Reciprocal Rank poäng till matrisfaktoriseringsmetoden och överträffar de popularitetsbaserade och slumpmässiga baslinjerna. De viktigaste fördelarna med vår modell är beräkningseffektivitet och dess förmåga att ge relevanta rekommendationer till nya användare utan behov av omskolning av modellen, vilket är nyckelfunktioner för verkliga användningsfall.
You, Di. "Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation". Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1321.
Texto completo da fonteLisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.
Texto completo da fonteRepresenting the information about music is a complex activity that involves different sub-tasks. This thesis manuscript mostly focuses on classical music, researching how to represent and exploit its information. The main goal is the investigation of strategies of knowledge representation and discovery applied to classical music, involving subjects such as Knowledge-Base population, metadata prediction, and recommender systems. We propose a complete workflow for the management of music metadata using Semantic Web technologies. We introduce a specialised ontology and a set of controlled vocabularies for the different concepts specific to music. Then, we present an approach for converting data, in order to go beyond the librarian practice currently in use, relying on mapping rules and interlinking with controlled vocabularies. Finally, we show how these data can be exploited. In particular, we study approaches based on embeddings computed on structured metadata, titles, and symbolic music for ranking and recommending music. Several demo applications have been realised for testing the previous approaches and resources
Livros sobre o assunto "Recommendation graph"
Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.
Texto completo da fonteVarlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Texto completo da fonteLevy, Barry S., ed. Social Injustice and Public Health. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190914653.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Recommendation graph"
Lodhi, Aminah Bilal, Muhammad Abdullah Bilal, Hafiz Syed Muhammad Bilal, Kifayat Ullah Khan, Fahad Ahmed Satti, Shah Khalid e Sungyoung Lee. "PNRG: Knowledge Graph-Driven Methodology for Personalized Nutritional Recommendation Generation". In Digital Health Transformation, Smart Ageing, and Managing Disability, 230–38. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43950-6_20.
Texto completo da fonteShi, Chuan, Xiao Wang e Philip S. Yu. "Heterogeneous Graph Representation for Recommendation". In Artificial Intelligence: Foundations, Theory, and Algorithms, 175–208. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6166-2_7.
Texto completo da fonteZhang, Yuanyuan, Maosheng Sun, Xiaowei Zhang e Yonglong Zhang. "Multi-task Feature Learning for Social Recommendation". In Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction, 240–52. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6471-7_18.
Texto completo da fonteXue, Feng, Wenjie Zhou, Zikun Hong e Kang Liu. "Multi-stage Knowledge Propagation Network for Recommendation". In Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction, 253–64. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6471-7_19.
Texto completo da fonteTien, Dong Nguyen, e Hai Pham Van. "Graph Neural Network Combined Knowledge Graph for Recommendation System". In Computational Data and Social Networks, 59–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66046-8_6.
Texto completo da fonteGuo, Zengqiang, Yan Yang, Jijie Zhang, Tianqi Zhou e Bangyu Song. "Knowledge Graph Bidirectional Interaction Graph Convolutional Network for Recommendation". In Lecture Notes in Computer Science, 532–43. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15931-2_44.
Texto completo da fonteChatterjee, Aniruddha, Sagnik Biswas e M. Kanchana. "Patent Recommendation Engine Using Graph Database". In Computational Intelligence and Data Analytics, 475–86. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3391-2_36.
Texto completo da fonteLiufu, Yuanwei, e Hong Shen. "Social Recommendation via Graph Attentive Aggregation". In Parallel and Distributed Computing, Applications and Technologies, 369–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96772-7_34.
Texto completo da fonteZhu, Jinghua, Yanchang Cui, Zhuohao Zhang e Heran Xi. "Knowledge Graph Transformer for Sequential Recommendation". In Artificial Neural Networks and Machine Learning – ICANN 2023, 459–71. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44223-0_37.
Texto completo da fonteWen, Bo, Shumin Deng e Huajun Chen. "Knowledge-Enhanced Collaborative Meta Learner for Long-Tail Recommendation". In Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence, 322–33. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1964-9_26.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Recommendation graph"
Gôlo, Marcos P. S., Leonardo G. Moraes, Rudinei Goularte e Ricardo M. Marcacini. "One-Class Recommendation through Unsupervised Graph Neural Networks for Link Prediction". In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227810.
Texto completo da fonteTian, Yijun, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald Metoyer e Nitesh V. Chawla. "RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/481.
Texto completo da fonteSang, Lei, e Lei Li. "Neural Collaborative Recommendation with Knowledge Graph". In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00038.
Texto completo da fonteJin, Yuanyuan, Wei Zhang, Mingyou Sun, Xing Luo e Xiaoling Wang. "Neural Restaurant-aware Dish Recommendation". In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00090.
Texto completo da fonteCao, Bin, Jianwei Yin, Shuiguang Deng, Dongjing Wang e Zhaohui Wu. "Graph-based workflow recommendation". In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398466.
Texto completo da fonteYang, Kaige, e Laura Toni. "GRAPH-BASED RECOMMENDATION SYSTEM". In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018. http://dx.doi.org/10.1109/globalsip.2018.8646359.
Texto completo da fonteLi, Chaoliu, Lianghao Xia, Xubin Ren, Yaowen Ye, Yong Xu e Chao Huang. "Graph Transformer for Recommendation". In SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3539618.3591723.
Texto completo da fonteXia, Lianghao, Yizhen Shao, Chao Huang, Yong Xu, Huance Xu e Jian Pei. "Disentangled Graph Social Recommendation". In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023. http://dx.doi.org/10.1109/icde55515.2023.00180.
Texto completo da fonteDossena, Marco, Christopher Irwin e Luigi Portinale. "Graph-based Recommendation using Graph Neural Networks". In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00270.
Texto completo da fonteZhou, Chunyi, Yuanyuan Jin, Xiaoling Wang e Yingjie Zhang. "Conversational Music Recommendation based on Bandits". In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00016.
Texto completo da fonteRelatórios de organizações sobre o assunto "Recommendation graph"
Rinaudo, Christina, William Leonard, Jaylen Hopson, Christopher Morey, Robert Hilborn e Theresa Coumbe. Enabling understanding of artificial intelligence (AI) agent wargaming decisions through visualizations. Engineer Research and Development Center (U.S.), abril de 2024. http://dx.doi.org/10.21079/11681/48418.
Texto completo da fonte