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Meta Graph Learning for Long-tail Recommendation

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Fei WangAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 2512 - 2522

https://doi.org/10.1145/3580305.3599428

Published: 04 August 2023 Publication History

Abstract

Highly skewed long-tail item distribution commonly hurts model performance on tail items in recommendation systems, especially for graph-based recommendation models. We propose a novel idea to learn relations among items as an auxiliary graph to enhance the graph-based representation learning and make recommendations collectively in a coupled framework. This raises two challenges, 1) the long-tail downstream information may also bias the auxiliary graph learning, and 2) the learned auxiliary graph may cause negative transfer to the original user-item bipartite graph. We innovatively propose a novel Meta Graph Learning framework for long-tail recommendation (MGL) for solving both challenges. The meta-learning strategy is introduced to the learning of an edge generator, which is first tuned to reconstruct a debiased item co-occurrence matrix, and then virtually evaluated on generating item relations for recommendation. Moreover, we propose a popularity-aware contrastive learning strategy to prevent negative transfer by aligning the confident head item representations with those of the learned auxiliary graph. Experiments on public datasets demonstrate that our proposed model significantly outperforms strong baselines for tail items without compromising the overall performance.

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References

[1]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling Popularity Bias in Learning-to-Rank Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, August 27--31, 2017, Paolo Cremonesi, Francesco Ricci, Shlomo Berkovsky, and Alexander Tuzhilin (Eds.). ACM, 42--46.

Digital Library

[2]

Nesreen K. Ahmed, Ryan A. Rossi, John Boaz Lee, Theodore L. Willke, Rong Zhou, Xiangnan Kong, and Hoda Eldardiry. 2022. Role-Based Graph Embeddings. IEEE Trans. Knowl. Data Eng., Vol. 34, 5 (2022), 2401--2415.

[3]

David C. Anastasiu and George Karypis. 2015. L2Knng: Fast Exact K-Nearest Neighbor Graph Construction with L2-Norm Pruning. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015, James Bailey, Alistair Moffat, Charu C. Aggarwal, Maarten de Rijke, Ravi Kumar, Vanessa Murdock, Timos K. Sellis, and Jeffrey Xu Yu (Eds.). ACM, 791--800.

[4]

Alex Beutel, Ed Huai-hsin Chi, Zhiyuan Cheng, Hubert Pham, and John R. Anderson. 2017. Beyond Globally Optimal: Focused Learning for Improved Recommendations. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3--7, 2017, Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 203--212.

[5]

Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res., Vol. 16 (2002), 321--357.

[6]

Junkun Chen, Xipeng Qiu, Pengfei Liu, and Xuanjing Huang. 2018. Meta Multi-Task Learning for Sequence Modeling. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2--7, 2019, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 5070--5077.

[7]

Lei Chen, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2020a. Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7--12, 2020. AAAI Press, 27--34.

[8]

Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, and Hongbo Deng. 2020b. ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25--30, 2020, Jimmy X. Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 579--588.

Digital Library

[9]

Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge J. Belongie. 2019. Class-Balanced Loss Based on Effective Number of Samples. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16--20, 2019. Computer Vision Foundation / IEEE, 9268--9277.

[10]

Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6--11 August 2017 (Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, 1126--1135.

[11]

Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. 2019. Learning Discrete Structures for Graph Neural Networks. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9--15 June 2019, Long Beach, California, USA (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 1972--1982.

[12]

Yao Fu, Yansong Feng, and John P. Cunningham. 2019. Paraphrase Generation with Latent Bag of Words. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché -Buc, Emily B. Fox, and Roman Garnett (Eds.). 13623--13634.

[13]

Pallabi Ghosh, Nirat Saini, Larry S. Davis, and Abhinav Shrivastava. 2021. Learning Graphs for Knowledge Transfer With Limited Labels. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19--25, 2021. Computer Vision Foundation / IEEE, 11151--11161.

[14]

Asnat Greenstein-Messica and Lior Rokach. 2018. Personal price aware multi-seller recommender system: Evidence from eBay. Knowl. Based Syst., Vol. 150 (2018), 14--26.

Digital Library

[15]

William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 1024--1034.

[16]

Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li. 2008. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2008, part of the IEEE World Congress on Computational Intelligence, WCCI 2008, Hong Kong, China, June 1--6, 2008. IEEE, 1322--1328.

[17]

Haibo He and Edwardo A. Garcia. 2009. Learning from Imbalanced Data. IEEE Trans. Knowl. Data Eng., Vol. 21, 9 (2009), 1263--1284.

Digital Library

[18]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020. 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, SIGIR 2020, Virtual Event, China, July 25--30, 2020, Jimmy X. Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 639--648.

Digital Library

[19]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3--7, 2017, Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 173--182.

Digital Library

[20]

Kexin Huang and Marinka Zitnik. 2020. Graph Meta Learning via Local Subgraphs. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.).

[21]

Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, and Bin Luo. 2019. Semi-Supervised Learning With Graph Learning-Convolutional Networks. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16--20, 2019. Computer Vision Foundation / IEEE, 11313--11320.

[22]

Douwe Kiela, Changhan Wang, and Kyunghyun Cho. 2018. Dynamic Meta-Embeddings for Improved Sentence Representations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2019, Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun'ichi Tsujii (Eds.). Association for Computational Linguistics, 1466--1477.

[23]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net.

[24]

Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer, Vol. 42, 8 (2009), 30--37.

Digital Library

[25]

Walid Krichene and Steffen Rendle. 2020. On Sampled Metrics for Item Recommendation. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020, Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 1748--1757.

Digital Library

[26]

Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4--8, 2019, Ankur Teredesai, Vipin Kumar, Ying Li, Ró mer Rosales, Evimaria Terzi, and George Karypis (Eds.). ACM, 1073--1082.

Digital Library

[27]

Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales. 2018b. Learning to Generalize: Meta-Learning for Domain Generalization. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2--7, 2019, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 3490--3497.

[28]

Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2018a. Adaptive Graph Convolutional Neural Networks. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2--7, 2019, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 3546--3553.

[29]

Tingting Liang, Congying Xia, Yuyu Yin, and Philip S. Yu. 2020. Joint Training Capsule Network for Cold Start Recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25--30, 2020, Jimmy X. Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 1769--1772.

[30]

Siyi Liu and Yujia Zheng. 2020. Long-tail Session-based Recommendation. In RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22--26, 2020, Rodrygo L. T. Santos, Leandro Balby Marinho, Elizabeth M. Daly, Li Chen, Kim Falk, Noam Koenigstein, and Edleno Silva de Moura (Eds.). ACM, 509--514.

Digital Library

[31]

Yuanfu Lu, Yuan Fang, and Chuan Shi. 2020. Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020, Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 1563--1573.

[32]

Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, and Ed H. Chi. 2020. Off-policy Learning in Two-stage Recommender Systems. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20--24, 2020, Yennun Huang, Irwin King, Tie-Yan Liu, and Maarten van Steen (Eds.). ACM / IW3C2, 463--473.

[33]

Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. 2021. Long-tail learning via logit adjustment. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net.

[34]

Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Li Li, Kun Zhang, Jinmei Luo, Zhaojie Liu, and Yanlong Du. 2021. Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021, Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 1157--1166.

[35]

Yoon-Joo Park and Alexander Tuzhilin. 2008. The long tail of recommender systems and how to leverage it. In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, Lausanne, Switzerland, October 23--25, 2008, Pearl Pu, Derek G. Bridge, Bamshad Mobasher, and Francesco Ricci (Eds.). ACM, 11--18.

Digital Library

[36]

Juan-Manuel Pé rez-Rú a, Xiatian Zhu, Timothy M. Hospedales, and Tao Xiang. 2020. Incremental Few-Shot Object Detection. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13--19, 2020. Computer Vision Foundation / IEEE, 13843--13852.

[37]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18--21, 2009, Jeff A. Bilmes and Andrew Y. Ng (Eds.). AUAI Press, 452--461.

Digital Library

[38]

Aravind Sankar, Junting Wang, Adit Krishnan, and Hari Sundaram. 2021. ProtoCF: Prototypical Collaborative Filtering for Few-shot Recommendation. In RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021 - 1 October 2021, Humberto Jesú s Corona Pamp'i n, Martha A. Larson, Martijn C. Willemsen, Joseph A. Konstan, Julian J. McAuley, Jean Garcia-Gathright, Bouke Huurnink, and Even Oldridge (Eds.). ACM, 166--175.

[39]

Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, and Timothy P. Lillicrap. 2016. Meta-Learning with Memory-Augmented Neural Networks. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19--24, 2016 (JMLR Workshop and Conference Proceedings, Vol. 48), Maria-Florina Balcan and Kilian Q. Weinberger (Eds.). JMLR.org, 1842--1850.

[40]

Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017. Prototypical Networks for Few-shot Learning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 4077--4087.

[41]

Qiuling Suo, Jingyuan Chou, Weida Zhong, and Aidong Zhang. 2020. TAdaNet: Task-Adaptive Network for Graph-Enriched Meta-Learning. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020, Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 1789--1799.

[42]

Kaihua Tang, Jianqiang Huang, and Hanwang Zhang. 2020a. Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.).

[43]

Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu C. Aggarwal, Prasenjit Mitra, and Suhang Wang. 2020b. Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19--23, 2020, Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, and Philippe Cudré -Mauroux (Eds.). ACM, 1435--1444.

[44]

Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2017. Graph Convolutional Matrix Completion. CoRR, Vol. abs/1706.02263 (2017).

[45]

Laurens van der Maaten. 2014. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res., Vol. 15, 1 (2014), 3221--3245.

Digital Library

[46]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.

[47]

Maksims Volkovs, Guang Wei Yu, and Tomi Poutanen. 2017. DropoutNet: Addressing Cold Start in Recommender Systems. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 4957--4966.

[48]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21--25, 2019, Benjamin Piwowarski, Max Chevalier, É ric Gaussier, Yoelle Maarek, Jian-Yun Nie, and Falk Scholer (Eds.). ACM, 165--174.

Digital Library

[49]

Jun Wu, Jingrui He, and Jiejun Xu. 2019. DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4--8, 2019, Ankur Teredesai, Vipin Kumar, Ying Li, Ró mer Rosales, Evimaria Terzi, and George Karypis (Eds.). ACM, 406--415.

Digital Library

[50]

Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019. OpenReview.net.

[51]

Yonghui Yang, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2021. Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11--15, 2021, Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.). ACM, 71--80.

Digital Library

[52]

Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed H. Chi. 2019. Sampling-bias-corrected neural modeling for large corpus item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16--20, 2019, Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). ACM, 269--277.

Digital Library

[53]

Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. 2012. Challenging the Long Tail Recommendation. Proc. VLDB Endow., Vol. 5, 9 (2012), 896--907.

Digital Library

[54]

Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun, and Steven C. H. Hoi. 2020. Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020, Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 359--367.

[55]

Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, and Yiming Yang. 2020. Graph-Revised Convolutional Network. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14--18, 2020, Proceedings, Part III (Lecture Notes in Computer Science, Vol. 12459), Frank Hutter, Kristian Kersting, Jefrey Lijffijt, and Isabel Valera (Eds.). Springer, 378--393.

[56]

Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, and Ed H. Chi. 2021. A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation. In WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19--23, 2021, Jure Leskovec, Marko Grobelnik, Marc Najork, Jie Tang, and Leila Zia (Eds.). ACM / IW3C2, 2220--2231.

[57]

Guorui Zhou, Xiaoqiang Zhu, Chengru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19--23, 2019, Yike Guo and Faisal Farooq (Eds.). ACM, 1059--1068.

Digital Library

[58]

Yu Zhu, Jinghao Lin, Shibi He, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2020. Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning. IEEE Trans. Knowl. Data Eng., Vol. 32, 4 (2020), 631--644. io

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Index Terms

Meta Graph Learning for Long-tail Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
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Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

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Cited By

Wang YGuo CMa YFeng Q(2025)Recommendation feedback-based dynamic adaptive training for efficient social item recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125605262:COnline publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125605
Qiu ZXie ZJi ZMao YCheng K(2025)HGSMAP: a novel heterogeneous graph-based associative percept framework for scenario-based optimal model assignmentKnowledge and Information Systems10.1007/s10115-024-02251-y67:1(915-952)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10115-024-02251-y
Zhao FHuang CXu HYang WHan W(2024)RGMeta: Enhancing Cold-Start Recommendations with a Residual Graph Meta-Embedding ModelElectronics10.3390/electronics1317347313:17(3473)Online publication date: 1-Sep-2024
https://doi.org/10.3390/electronics13173473
Zhang PLin PLi DWang WQi XLi JXiong J(2024)MGACL: Prediction Drug–Protein Interaction Based on Meta-Graph Association-Aware Contrastive LearningBiomolecules10.3390/biom1410126714:10(1267)Online publication date: 8-Oct-2024
https://doi.org/10.3390/biom14101267
Balasubramanian KAlshabanah AMarkowitz EVer Steeg GAnnavaram M(2024)Biased User History Synthesis for Personalized Long-Tail Item RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688141(189-199)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688141
Kim JKim EYeo KJeon YKim CLee SLee JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Content-based Graph Reconstruction for Cold-start Item RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657801(1263-1273)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657801
Huang XLiu XHe TXu Z(2024)Fair Recommendation Method Based on Meta-Learning and Feature Enhancement2024 Twelfth International Conference on Advanced Cloud and Big Data (CBD)10.1109/CBD65573.2024.00064(321-326)Online publication date: 28-Nov-2024
https://doi.org/10.1109/CBD65573.2024.00064

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