Fetching the paper…
Reading the bibliography…
Recommendation for new users, also called user cold start, has been a well-recognized challenge for online recommender systems.
Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling
Guorui Zhou, Kailun Wu, Weijie Bian, Zhao Yang, Xiaoqiang Zhu, and Kun Gai. 2019 · 1906
Earlier work this paper cites.
Modeling Task Relationships in Multi-Task Learning with Multi-Gate Mixture-of-Experts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18) . Association for Computing Machinery, New York, NY, USA, 1930–1939
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H. Chi. 2018 · 1939
Earlier work this paper cites.
A Perspective View and Survey of Meta-Learning
Ricardo Vilalta and Youssef Drissi. 2002 · 2002
Earlier work this paper cites.
Learning Factored Representations in a Deep Mixture of Experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever. 2014 · 2014
Earlier work this paper cites.
Dealing with the new user cold-start problem in recommender systems: A comparative review
Le Hoang Son. 2016 · 2014
Earlier work this paper cites.
Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models. In 2014 IEEE Spoken Language Technology Workshop (SLT) . 171–176
Pawel Swietojanski and Steve Renals. 2014 · 2014
Earlier work this paper cites.
The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan. 2015 · 2015
Earlier work this paper cites.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 70) , Doina Precup and Yee Whye Teh (Eds.). PMLR, 1126–1135
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and Jeff Dean. 2017 · 2017
Earlier work this paper cites.
A Meta-Learning Perspective on Cold-Start Recommendations for Items. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle. 2017 · 2017
Cited alongside, same era.
Attention is All you Need. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
DropoutNet: Addressing Cold Start in Recommender Systems. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Maksims Volkovs, Guangwei Yu, and Tomi Poutanen. 2017 · 2017
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 . ACM, 1073–1082
Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019 · 2019
Later among the works it cites.
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 (Paris, France) (SIGIR’19) . Association for Computing Machinery, New York, NY, USA, 695–704
Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, and Qing He. 2019 · 2019
Later among the works it cites.
What You Look Matters? Offline Evaluation of Advertising Creatives for Cold-Start Problem. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM ’19) . Association for Computing Machinery, New York, NY, USA, 2605–2613
Zhichen Zhao, Lei Li, Bowen Zhang, Meng Wang, Yuning Jiang, Li Xu, Fengkun Wang, and Weiying Ma. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Handling User Cold Start Problem in Recommender Systems Using Fuzzy Clustering. In Information and Communication Technology for Sustainable Development , Durgesh Kumar Mishra, Malaya Kumar Nayak, and Amit Joshi (Eds.). Springer Singapore, Singapore, 143–151
Sugandha Gupta and Shivani Goel. 2018 · 2018
Cited alongside, same era.
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
Yu Wang, Jixing Xu, Aohan Wu, Mantian Li, Yang He, Jinghe Hu, and Weipeng Yan. 2018 · 2018
Cited alongside, same era.
Addressing the Item Cold-start Problem by Attribute-driven Active Learning
Yu Zhu, Jinhao Lin, Shibi He, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2018 · 2018
Cited alongside, same era.
Behavior Sequence Transformer for E-Commerce Recommendation in Alibaba. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (Anchorage, Alaska) (DLP-KDD ’19) . Association for Computing Machinery, New York, NY, USA, Article 12, 4 pages
Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. 2019 · 2019
Cited alongside, same era.
Sequential Scenario-Specific Meta Learner for Online Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19) . Association for Computing Machinery, New York, NY, USA, 2895–2904
Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, and Jie Tang. 2019 · 2019
Cited alongside, same era.
MAMO: Memory-Augmented Meta-Optimization for Cold-Start Recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Virtual Event, CA, USA) (KDD ’20) . Association for Computing Machinery, New York, NY, USA, 688–697
Manqing Dong, Feng Yuan, Lina Yao, Xiwei Xu, and Liming Zhu. 2020 · 2020
Later among the works it cites.
Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 319–328
Shaoyun Shi, Weizhi Ma, Min Zhang, Yongfeng Zhang, Xinxing Yu, Houzhi Shan, Yiqun Liu, and Shaoping Ma. 2020 · 2020
Later among the works it cites.
Meta-Learning in Neural Networks: A Survey
T. M. Hospedales, A. Antoniou, P. Micaelli, and A. J. Storkey. 5555 · 2021
Closest in time.
Learning to Warm Up Cold Item Embeddings for Cold-Start Recommendation with Meta Scaling and Shifting Networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 1167–1176
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang, Kaikai Ge, Ying Sun, Xu Zhang, Leyu Lin, and Juan Cao. 2021 · 2021
Closest in time.
Image Matters: Visually Modeling User Behaviors Using Advanced Model Server. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM ’18) . Association for Computing Machinery, New York, NY, USA, 2087–2095
Tiezheng Ge, Liqin Zhao, Guorui Zhou, Keyu Chen, Shuying Liu, Huimin Yi, Zelin Hu, Bochao Liu, Peng Sun, Haoyu Liu, Pengtao Yi, Sui Huang, Zhiqiang Zhang, Xiaoqiang Zhu, Yu Zhang, and Kun Gai. 2018 · 2095
Closest in time.