Fetching the paper…
Reading the bibliography…
Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, predominantly depend on sparse ID features in their models.
Methods and metrics for cold-start recommendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval . 253–260
Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock. 2002 · 2002
Earlier work this paper cites.
Multimedia features for click prediction of new ads in display advertising. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 777–785
Haibin Cheng, Roelof van Zwol, Javad Azimi, Eren Manavoglu, Ruofei Zhang, Yang Zhou, and Vidhya Navalpakkam. 2012 · 2012
Earlier work this paper cites.
Image Feature Learning for Cold Start Problem in Display Advertising. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence . 3728–3734
Kaixiang Mo, Bo Liu, Lei Xiao, Yong Li, and Jie Jiang. 2015 · 2015
Earlier work this paper cites.
FaceNet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 815–823
Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015 · 2015
Earlier work this paper cites.
Deep CTR Prediction in Display Advertising. In Proceedings of the 2016 ACM Conference on Multimedia Conference . 811–820
Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, and Xian-Sheng Hua. 2016 · 2016
Earlier work this paper cites.
Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems . ACM, 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016 · 2016
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.
Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In Proceedings of the 25th International Conference on World Wide Web . 507–517
Ruining He and Julian J. McAuley. 2016 · 2016
Earlier work this paper cites.
Images Don’t Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 541–548
Corey Lynch, Kamelia Aryafar, and Josh Attenberg. 2016 · 2016
Earlier work this paper cites.
On Calibration of Modern Neural Networks. In Proceedings of the 34th International Conference on Machine Learning , Vol. 70. 1321–1330
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 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 . 2191–2200
Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, and Kun Gai. 2017 · 2017
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 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 . ACM, 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
Cited alongside, same era.
Learning Tree-based Deep Model for Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . London, UK, 1079–1088
Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018 · 2018
Cited alongside, same era.
XDL: An Industrial Deep Learning Framework for High-Dimensional Sparse Data. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data . 1–9
Biye Jiang, Chao Deng, Huimin Yi, Zelin Hu, Guorui Zhou, Yang Zheng, Sui Huang, Xinyang Guo, Dongyue Wang, Yue Song, et al · 2019
Cited alongside, same era.
Deep Interest Evolution Network for Click-Through Rate Prediction. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence . Honolulu, Hawaii, USA, 5941–5948
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019 · 2019
CAN: Feature Co-Action Network for Click-Through Rate Prediction. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining . 57–65
Weijie Bian, Kailun Wu, Lejian Ren, Qi Pi, Yujing Zhang, Can Xiao, Xiang-Rong Sheng, Yong-Nan Zhu, Zhangming Chan, Na Mou, Xinchen Luo, Shiming Xiang, Guorui Zhou, Xiaoqiang Zhu, and Hongbo Deng. 2022 · 2022
Later among the works it cites.
End-to-End Image-Based Fashion Recommendation
Shereen Elsayed, Lukas Brinkmeyer, and Lars Schmidt-Thieme. 2022 · 2022
Later among the works it cites.
PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems. In 2022 IEEE 38th International Conference on Data Engineering (ICDE) . 3453–3466
Yuanxing Zhang, Langshi Chen, Siran Yang, Man Yuan, Huimin Yi, et al · 2022
Later among the works it cites.
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 3774–3784
Zhangming Chan, Yu Zhang, Shuguang Han, Yong Bai, Xiang-Rong Sheng, Siyuan Lou, Jiacen Hu, Baolin Liu, Yuning Jiang, Jian Xu, and Bo Zheng. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Selection and generation: Learning towards multi-product advertisement post generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 3818–3829
Zhangming Chan, Yuchi Zhang, Xiuying Chen, Shen Gao, Zhiqiang Zhang, Dongyan Zhao, and Rui Yan. 2020 · 2020
Cited alongside, same era.
Momentum Contrast for Unsupervised Visual Representation Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9726–9735
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. 2020 · 2020
Cited alongside, same era.
Embedding-based Retrieval in Facebook Search. In Proceedings of The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2553–2561
Jui-Ting Huang, Ashish Sharma, Shuying Sun, Li Xia, David Zhang, Philip Pronin, Janani Padmanabhan, Giuseppe Ottaviano, and Linjun Yang. 2020 · 2020
Cited alongside, same era.
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest. In Proceedings of The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2311–2320
Aditya Pal, Chantat Eksombatchai, Yitong Zhou, Bo Zhao, Charles Rosenberg, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
COLD: Towards the Next Generation of Pre-Ranking System
Zhe Wang, Liqin Zhao, Biye Jiang, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. 2020 · 2020
Cited alongside, same era.
Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling. In Proceedings of The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2890–2898
Siyu Gu, Xiang-Rong Sheng, Ying Fan, Guorui Zhou, and Xiaoqiang Zhu. 2021 · 2021
Cited alongside, same era.
Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning , Vol. 139. 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction. In Proceedings of The 30th ACM International Conference on Information and Knowledge Management . 4104–4113
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, and Xiaoqiang Zhu. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
PS-SA: An Efficient Self-Attention via Progressive Sampling for User Behavior Sequence Modeling. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . 4639–4645
Jiacen Hu, Zhangming Chan, Yu Zhang, Shuguang Han, Siyuan Lou, Baolin Liu, Han Zhu, Yuning Jiang, Jian Xu, and Bo Zheng. 2023 · 2023
Later among the works it cites.
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4813–4822
Xiang-Rong Sheng, Jingyue Gao, Yueyao Cheng, Siran Yang, Shuguang Han, Hongbo Deng, Yuning Jiang, Jian Xu, and Bo Zheng. 2023 · 2023
Later among the works it cites.
Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
Anima Singh, Trung Vu, Raghunandan H. Keshavan, Nikhil Mehta, Xinyang Yi, Lichan Hong, Lukasz Heldt, Li Wei, Ed H. Chi, and Maheswaran Sathiamoorthy. 2023 · 2023
Later among the works it cites.
COURIER: Contrastive User Intention Reconstruction for Large-Scale Pre-Train of Image Features
Jia-Qi Yang, Chenglei Dai, Dan Ou, Ju Huang, De-Chuan Zhan, Qingwen Liu, Xiaoyi Zeng, and Yang Yang. 2023 · 2023
Later among the works it cites.
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2639–2649
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023 · 2023
Later among the works it cites.
COPR: Consistency-Oriented Pre-Ranking for Online Advertising. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . 4974–4980
Zhishan Zhao, Jingyue Gao, Yu Zhang, Shuguang Han, Siyuan Lou, Xiang-Rong Sheng, Zhe Wang, Han Zhu, Yuning Jiang, Jian Xu, and Bo Zheng. 2023 · 2023
Later among the works it cites.
Adversarial gradient driven exploration for deep click-through rate prediction. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2050–2058
Kailun Wu, Weijie Bian, Zhangming Chan, Lejian Ren, Shiming Xiang, Shu-Guang Han, Hongbo Deng, and Bo Zheng. 2022 · 2058
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 . 2087–2095
Tiezheng Ge, Liqin Zhao, Guorui Zhou, Keyu Chen, Shuying Liu, Huiming 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.