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Recommending cold-start items is a long-standing and fundamental challenge in recommender systems.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Combining content and collaboration in text filtering. In Proceedings of International Joint Conferences on Artificial Intelligence , Vol. 99. 86–91
Ian Soboroff and Charles Nicholas. 1999 · 1999
Earlier work this paper cites.
Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD . 426–434
Yehuda Koren. 2008 · 2008
Earlier work this paper cites.
Probabilistic matrix factorization. In Advances in neural information processing systems . 1257–1264
Andriy Mnih and Russ R Salakhutdinov. 2008 · 2008
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the International Conference on Artificial Intelligence and statistics . 249–256
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
Improving pairwise learning for item recommendation from implicit feedback. In WSDM . 273–282
Steffen Rendle and Christoph Freudenthaler. 2014 · 2014
Earlier work this paper cites.
Item cold-start recommendations: learning local collective embeddings. In Proceedings of ACM Conference on Recommender systems . 89–96
Martin Saveski and Amin Mantrach. 2014 · 2014
Earlier work this paper cites.
Learning image and user features for recommendation in social networks. In Proceedings of the IEEE International Conference on Computer Vision . 4274–4282
Xue Geng, Hanwang Zhang, Jingwen Bian, and Tat-Seng Chua. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization. In Proceedings of International Conference on Learning Representations . 1–16
Diederik P Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
A simple but tough-to-beat baseline for sentence embeddings. In Proceedings of International Conference on Learning Representations . 1–16
Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2016 · 2016
Earlier work this paper cites.
Cold-start recommendation with provable Guarantees: A decoupled approach
Iman Barjasteh, Rana Forsati, Dennis Ross, Abdol-Hossein Esfahanian, and Hayder Radha. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Neural collaborative filtering. In Proceedings of 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.
CNN architectures for large-scale audio classification. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing . 131–135
Shawn Hershey, Sourish Chaudhuri, Daniel PW Ellis, Jort F Gemmeke, Aren Jansen, R Channing Moore, Manoj Plakal, Devin Platt, Rif A Saurous, Bryan Seybold, et al · 2017
Earlier work this paper cites.
A meta-learning perspective on cold-start recommendations for items
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle. 2017 · 2017
Earlier work this paper cites.
Dropoutnet: Addressing cold start in recommender systems. In Advances in Neural Information Processing Systems . 4957–4966
Maksims Volkovs, Guangwei Yu, and Tomi Poutanen. 2017 · 2017
Cited alongside, same era.
Cross-Modal Moment Localization in Videos. In Proceedings of the 26th ACM International Conference on Multimedia . 843–851
Meng Liu, Xiang Wang, Liqiang Nie, Qi Tian, Baoquan Chen, and Tat-Seng Chua. 2018 · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Cited alongside, same era.
CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations. In Proceedings of ACM Conference on Recommender Systems . 228–236
Oren Barkan, Noam Koenigstein, Eylon Yogev, and Ori Katz. 2019 · 2019
Cited alongside, same era.
Movie genome: alleviating new item cold start in movie recommendation
InfoXLM: An information-theoretic framework for cross-lingual language model pre-training
Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, and Ming Zhou. 2020 · 2020
Later among the works it cites.
Recommender systems leveraging multimedia content
Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, and Gabriella Pasi. 2020 · 2020
Later among the works it cites.
How to Learn Item Representation for Cold-Start Multimedia Recommendation?. In Proceedings of ACM International Conference on Multimedia . 3469–3477
Xiaoyu Du, Xiang Wang, Xiangnan He, Zechao Li, Jinhui Tang, and Tat-Seng Chua. 2020 · 2020
Later among the works it cites.
Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
Later among the works it cites.
Context-aware graph label propagation network for saliency detection
Wei Ji, Xi Li, Lina Wei, Fei Wu, and Yueting Zhuang. 2020 · 2020
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Yashar Deldjoo, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-Zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, and Paolo Cremonesi. 2019 · 2019
Cited alongside, same era.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2019 · 2019
Cited alongside, same era.
Human-centric clothing segmentation via deformable semantic locality-preserving network
Wei Ji, Xi Li, Fei Wu, Zhijie Pan, and Yueting Zhuang. 2019 · 2019
Cited alongside, same era.
MeLU: meta-learned user preference estimator for cold-start recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1073–1082
Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019 · 2019
Cited alongside, same era.
From zero-shot learning to cold-start recommendation. In Proceedings of AAAI Conference on Artificial Intelligence , Vol. 33. 4189–4196
Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang, and Zi Huang. 2019 · 2019
Cited alongside, same era.
User Diverse Preference Modeling by Multimodal Attentive Metric Learning. In Proceedings of the 27th ACM International Conference on Multimedia . ACM, 1526–1534
Fan Liu, Zhiyong Cheng, Changchang Sun, Yinglong Wang, Liqiang Nie, and Mohan Kankanhalli. 2019 · 2019
Cited alongside, same era.
Warm up cold-start advertisements: Improving ctr predictions via learning to learn id embeddings. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 695–704
Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, and Qing He. 2019 · 2019
Cited alongside, same era.
Adaptive Feature Sampling for Recommendation with Missing Content Feature Values. In Proceedings of ACM International Conference on Information and Knowledge Management . 1451–1460
Shaoyun Shi, Min Zhang, Xinxing Yu, Yongfeng Zhang, Bin Hao, Yiqun Liu, and Shaoping Ma. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
What Aspect Do You Like: Multi-scale Time-aware User Interest Modeling for Micro-video Recommendation. In Proceedings of the 28th ACM International Conference on Multimedia . 3487–3495
Hao Jiang, Wenjie Wang, Yinwei Wei, Zan Gao, Yinglong Wang, and Liqiang Nie. 2020 · 2020
Later among the works it cites.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2020
Later among the works it cites.
Meta-learning on heterogeneous information networks for cold-start recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1563–1573
Yuanfu Lu, Yuan Fang, and Chuan Shi. 2020 · 2020
Later among the works it cites.
Reinforced negative sampling over knowledge graph for recommendation. In Proceedings of The Web Conference 2020 . 99–109
Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, and Tat-Seng Chua. 2020 · 2020
Later among the works it cites.
Personalized Item Recommendation for Second-hand Trading Platform. In Proceedings of the 28th ACM International Conference on Multimedia . 3478–3486
Xuzheng Yu, Tian Gan, Yinwei Wei, Zhiyong Cheng, and Liqiang Nie. 2020 · 2020
Later among the works it cites.
Contrastive learning for debiased candidate generation in large-scale recommender systems
Chang Zhou, Jianxin Ma, Jianwei Zhang, Jingren Zhou, and Hongxia Yang. 2020 · 2020
Later among the works it cites.
Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. In Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval . 1121–1130
Ziwei Zhu, Shahin Sefati, Parsa Saadatpanah, and James Caverlee. 2020 · 2020
Later among the works it cites.
Smart Contract Vulnerability Detection using Graph Neural Network. In IJCAI . 3283–3290
Yuan Zhuang, Zhenguang Liu, Peng Qian, Qi Liu, Xiang Wang, and Qinming He. 2020 · 2020
Later among the works it cites.
Interest-Aware Message-Passing GCN for Recommendation. In Proceedings of the Web Conference 2021 . ACM, 1296–1305
Fan Liu, Zhiyong Cheng, Lei Zhu, Zan Gao, and Liqiang Nie. 2021a · 2021
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Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection
Zhenguang Liu, Peng Qian, Xiaoyang Wang, Yuan Zhuang, Lin Qiu, and Xun Wang. 2021b · 2021
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Hierarchical User Intent Graph Network for Multimedia Recommendation
Yinwei Wei, Xiang Wang, Xiangnan He, Liqiang Nie, Yong Rui, and Tat-Seng Chua. 2021 · 2021
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