Fetching the paper…
Reading the bibliography…
Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Introduction to the Theory of Computation
Michael Sipser. 1996 · 1996
Earlier work this paper cites.
E-commerce recommendation applications
J Ben Schafer, Joseph A Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
A survey of music recommendation systems and future perspectives. In 9th international symposium on computer music modeling and retrieval , Vol. 4. 395–410
Yading Song, Simon Dixon, and Marcus Pearce. 2012 · 2012
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . 701–710
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
Earlier work this paper cites.
Trirank: Review-aware explainable recommendation by modeling aspects. In Proceedings of the 24th ACM international on conference on information and knowledge management . 1661–1670
Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015 · 2015
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Earlier work this paper cites.
Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI conference on artificial intelligence , Vol. 29
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015 · 2015
Earlier work this paper cites.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining . 855–864
Aditya Grover and Jure Leskovec. 2016 · 2016
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
Earlier work this paper cites.
Optimized Cost per Click in Taobao Display Advertising. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD ’17) . Association for Computing Machinery, New York, NY, USA, 2191–2200
Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, and Kun Gai. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
Data-driven graph construction and graph learning: A review
Lishan Qiao, Limei Zhang, Songcan Chen, and Dinggang Shen. 2018 · 2018
Earlier work this paper cites.
Heterogeneous information network embedding for recommendation
Chuan Shi, Binbin Hu, Wayne Xin Zhao, and S Yu Philip. 2018 · 2018
Earlier work this paper cites.
Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM international conference on information and knowledge management . 417–426
Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018 · 2018
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018 · 2018
Earlier work this paper cites.
Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Earlier work this paper cites.
Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1059–1068
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
Earlier work this paper cites.
Hierarchical clustering: Objective functions and algorithms
Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, and Claire Mathieu. 2019 · 2019
Earlier work this paper cites.
Multi-interest network with dynamic routing for recommendation at Tmall. In Proceedings of the 28th ACM international conference on information and knowledge management . 2615–2623
Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019 · 2019
Earlier work this paper cites.
MindSpore
2020 · 2020
Earlier work this paper cites.
Knowledge graph completion: A review
Zhe Chen, Yuehan Wang, Bin Zhao, Jing Cheng, Xin Zhao, and Zongtao Duan. 2020 · 2020
Cited alongside, same era.
A review of movie recommendation system: Limitations, Survey and Challenges
Mahesh Goyani and Neha Chaurasiya. 2020 · 2020
Cited alongside, same era.
Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Cited alongside, same era.
Multi-behavior recommendation with graph convolutional networks. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval . 659–668
Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li. 2020 · 2020
Cited alongside, same era.
Next-item recommendation with sequential hypergraphs. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval . 1101–1110
C-Pack: Packaged Resources To Advance General Chinese Embedding
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. 2023 · 2023
Later among the works it cites.
E-commerce Search via Content Collaborative Graph Neural Network. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2885–2897
Guipeng Xv, Chen Lin, Wanxian Guan, Jinping Gou, Xubin Li, Hongbo Deng, Jian Xu, and Bo Zheng. 2023 · 2023
Later among the works it cites.
Making large language models perform better in knowledge graph completion
Yichi Zhang, Zhuo Chen, Wen Zhang, and Huajun Chen. 2023 · 2023
Later among the works it cites.
A comprehensive survey on automatic knowledge graph construction
Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, and Xindong Wu. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, and James Caverlee. 2020 · 2020
Cited alongside, same era.
Sequential recommendation with graph neural networks. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval . 378–387
Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021 · 2021
Cited alongside, same era.
An adversarial imitation click model for information retrieval. In Proceedings of the Web Conference 2021 . 1809–1820
Xinyi Dai, Jianghao Lin, Weinan Zhang, Shuai Li, Weiwen Liu, Ruiming Tang, Xiuqiang He, Jianye Hao, Jun Wang, and Yong Yu. 2021 · 2021
Cited alongside, same era.
Retagnn: Relational temporal attentive graph neural networks for holistic sequential recommendation. In Proceedings of the web conference 2021 . 2968–2979
Cheng Hsu and Cheng-Te Li. 2021 · 2021
Cited alongside, same era.
A survey on locality sensitive hashing algorithms and their applications
Omid Jafari, Preeti Maurya, Parth Nagarkar, Khandker Mushfiqul Islam, and Chidambaram Crushev. 2021 · 2021
Cited alongside, same era.
A survey on knowledge graphs: Representation, acquisition, and applications
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip. 2021 · 2021
Cited alongside, same era.
A Graph-Enhanced Click Model for Web Search. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1259–1268
Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Shuai Li, Ruiming Tang, Xiuqiang He, Jianye Hao, and Yong Yu. 2021 · 2021
Cited alongside, same era.
Retrieval & interaction machine for tabular data prediction. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 1379–1389
Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Ruiming Tang, Xiuqiang He, and Yong Yu. 2021 · 2021
Cited alongside, same era.
Hongyu Zhou, Xin Zhou, Zhiwei Zeng, Lingzi Zhang, and Zhiqi Shen. 2023 · 2023
Later among the works it cites.
Large language models for mathematical reasoning: Progresses and challenges
Janice Ahn, Rishu Verma, Renze Lou, Di Liu, Rui Zhang, and Wenpeng Yin. 2024 · 2024
Closest in time.
Codekgc: Code language model for generative knowledge graph construction
Zhen Bi, Jing Chen, Yinuo Jiang, Feiyu Xiong, Wei Guo, Huajun Chen, and Ningyu Zhang. 2024 · 2024
Closest in time.
A survey of sequential recommendation systems: Techniques, evaluation, and future directions
Tesfaye Fenta Boka, Zhendong Niu, and Rama Bastola Neupane. 2024 · 2024
Closest in time.
Relation labeling in product knowledge graphs with large language models for e-commerce
Jiao Chen, Luyi Ma, Xiaohan Li, Jianpeng Xu, Jason HD Cho, Kaushiki Nag, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2024 · 2024
Closest in time.
Towards graph foundation models for personalization. In Companion Proceedings of the ACM Web Conference 2024 . 1798–1802
Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, and Mounia Lalmas. 2024 · 2024
Closest in time.
Personalized audiobook recommendations at spotify through graph neural networks. In Companion Proceedings of the ACM on Web Conference 2024 . 403–412
Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, et al · 2024
Closest in time.
Automated Construction of Theme-specific Knowledge Graphs
Linyi Ding, Sizhe Zhou, Jinfeng Xiao, and Jiawei Han. 2024 · 2024
Closest in time.
Integrating Large Language Models with Graphical Session-Based Recommendation
Naicheng Guo, Hongwei Cheng, Qianqiao Liang, Linxun Chen, and Bing Han. 2024a · 2024
Closest in time.
Graphedit: Large language models for graph structure learning
Zirui Guo, Lianghao Xia, Yanhua Yu, Yuling Wang, Zixuan Yang, Wei Wei, Liang Pang, Tat-Seng Chua, and Chao Huang. 2024b · 2024
Closest in time.
A Survey on Large Language Models for Code Generation
Juyong Jiang, Fan Wang, Jiasi Shen, Sungju Kim, and Sunghun Kim. 2024 · 2024
Closest in time.
A survey of generative search and recommendation in the era of large language models
Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2024 · 2024
Closest in time.
ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction. In Proceedings of the ACM on Web Conference 2024 . 3319–3330
Jianghao Lin, Bo Chen, Hangyu Wang, Yunjia Xi, Yanru Qu, Xinyi Dai, Kangning Zhang, Ruiming Tang, Yong Yu, and Weinan Zhang. 2024a · 2024
Closest in time.
How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong Liu, Chuhan Wu, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, and Weinan Zhang. 2024b · 2024
Closest in time.
Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation. In Proceedings of the ACM on Web Conference 2024 . 3497–3508
Jianghao Lin, Rong Shan, Chenxu Zhu, Kounianhua Du, Bo Chen, Shigang Quan, Ruiming Tang, Yong Yu, and Weinan Zhang. 2024c · 2024
Closest in time.
Mamba4rec: Towards efficient sequential recommendation with selective state space models
Chengkai Liu, Jianghao Lin, Jianling Wang, Hanzhou Liu, and James Caverlee. 2024c · 2024
Closest in time.
Vector Quantization for Recommender Systems: A Review and Outlook
Qijiong Liu, Xiaoyu Dong, Jiaren Xiao, Nuo Chen, Hengchang Hu, Jieming Zhu, Chenxu Zhu, Tetsuya Sakai, and Xiao-Ming Wu. 2024a · 2024
Closest in time.
Discrete Semantic Tokenization for Deep CTR Prediction. In Companion Proceedings of the ACM on Web Conference 2024 . 919–922
Qijiong Liu, Hengchang Hu, Jiahao Wu, Jieming Zhu, Min-Yen Kan, and Xiao-Ming Wu. 2024b · 2024
Closest in time.
Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training. In Proceedings of the ACM on Web Conference 2024 . 3940–3951
Ngoc-Hieu Nguyen, Tuan-Anh Nguyen, Tuan Nguyen, Vu Tien Hoang, Dung D Le, and Kok-Seng Wong. 2024 · 2024
Closest in time.
Unifying large language models and knowledge graphs: A roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2024 · 2024
Closest in time.
Recommender systems with generative retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al · 2024
Closest in time.
A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications
Wenbo Shang and Xin Huang. 2024 · 2024
Closest in time.
Llmrec: Large language models with graph augmentation for recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining . 806–815
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024 · 2024
Closest in time.
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, and Enhong Chen. 2024 · 2024
Closest in time.
Give us the facts: Enhancing large language models with knowledge graphs for fact-aware language modeling
Linyao Yang, Hongyang Chen, Zhao Li, Xiao Ding, and Xindong Wu. 2024a · 2024
Closest in time.
Qian Zhao, Hao Qian, Ziqi Liu, Gong-Duo Zhang, and Lihong Gu. 2024 · 2024
Closest in time.