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Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.
The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime Carbonell and Jade Goldstein · 1998
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Gaussian Processes for Machine Learning
C. E. Rasmussen and C. I. K. Williams · 2006
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Modularity and community detection in bipartite networks
Michael J Barber · 2007
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Avoiding monotony: improving the diversity of recommendation lists
Mi Zhang and Neil Hurley · 2008
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Diversification and refinement in collaborative filtering recommender
Rubi Boim, Tova Milo, and Slava Novgorodov · 2011
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Max-sum diversification, monotone submodular functions and dynamic updates
Allan Borodin, Hyun Chul Lee, and Yuli Ye · 2012
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Determinantal point processes for machine learning
Alex Kulesza, Ben Taskar, et al · 2012
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Ad click prediction: a view from the trenches
H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, et al · 2013
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Promoting diversity in recommendation by entropy regularizer
Lijing Qin and Xiaoyan Zhu · 2013
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Optimal greedy diversity for recommendation
Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, and Zheng Wen · 2015
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Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
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Product-based neural networks for user response prediction
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang · 2016
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A framework for recommending relevant and diverse items
Chaofeng Sha, Xiaowei Wu, and Junyu Niu · 2016
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Learning to recommend accurate and diverse items
Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, and Hui Xiong · 2017
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Deepfm: A factorization-machine based neural network for ctr prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 2017
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IR evaluation methods for retrieving highly relevant documents
Kalervo Järvelin and Jaana Kekäläinen · 2017
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Learning to diversify search results via subtopic attention
Zhengbao Jiang, Ji-Rong Wen, Zhicheng Dou, Wayne Xin Zhao, Jian-Yun Nie, and Ming Yue · 2017
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Learning a deep listwise context model for ranking refinement
Qingyao Ai, Keping Bi, Jiafeng Guo, and W Bruce Croft · 2018
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Seq2slate: Re-ranking and slate optimization with rnns
Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Chi, Elad Eban, Xiyang Luo, Alan Mackey, and Ofer Meshi · 2018
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Fast greedy map inference for determinantal point process to improve recommendation diversity
Laming Chen, Guoxin Zhang, and Eric Zhou · 2018
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Deep determinantal point processes
Mike Gartrell, Elvis Dohmatob, and Jon Alberdi · 2018
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Improving performances of top-n recommendations with co-clustering method
Liang Feng, Qianchuan Zhao, and Cangqi Zhou · 2020
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Enhancing recommendation diversity using determinantal point processes on knowledge graphs
Lu Gan, Diana Nurbakova, Léa Laporte, and Sylvie Calabretto · 2020
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Personalized flight itinerary ranking at fliggy
Jinhong Huang, Yang Li, Shan Sun, Bufeng Zhang, and Jin Huang · 2020
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Diversified interactive recommendation with implicit feedback
Yong Liu, Yingtai Xiao, Qiong Wu, Chunyan Miao, Juyong Zhang, Binqiang Zhao, and Haihong Tang · 2020
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Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction
Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai · 2020
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Practical diversified recommendations on youtube with determinantal point processes
Mark Wilhelm, Ajith Ramanathan, Alexander Bonomo, Sagar Jain, Ed H Chi, and Jennifer Gillenwater · 2018
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Deep interest network for click-through rate prediction
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai · 2018
Cited alongside, same era.
Globally optimized mutual influence aware ranking in e-commerce search
Tao Zhuang, Wenwu Ou, and Zhirong Wang · 2018
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Multi-interest network with dynamic routing for recommendation at tmall
Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee · 2019
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Personalized re-ranking for recommendation
Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, et al · 2019
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Practice on long sequential user behavior modeling for click-through rate prediction
Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai · 2019
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Jiarui Qin, Weinan Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, and Yong Yu · 2020
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A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks
Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, et al · 2020
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GRN: Generative rerank network for context-wise recommendation
Yufei Feng, Binbin Hu, Yu Gong, Fei Sun, Qingwen Liu, and Wenwu Ou · 2021
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Re-ranking with constraints on diversified exposures for homepage recommender system
Qi Hao, Tianze Luo, and Guangda Huzhang · 2021
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Sliding spectrum decomposition for diversified recommendation
Yanhua Huang, Weikun Wang, Lei Zhang, and Ruiwen Xu · 2021
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Variation control and evaluation for generative slate recommendations
Shuchang Liu, Fei Sun, Yingqiang Ge, Changhua Pei, and Yongfeng Zhang · 2021
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DGCN: Diversified recommendation with graph convolutional networks
Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, and Yong Li · 2021
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Efficient long sequential user data modeling for click-through rate prediction
Qiwei Chen, Yue Xu, Changhua Pei, Shanshan Lv, Tao Zhuang, and Junfeng Ge · 2022
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Feature-aware diversified re-ranking with disentangled representations for relevant recommendation
Zihan Lin, Hui Wang, Jingshu Mao, Wayne Xin Zhao, Cheng Wang, Peng Jiang, and Ji-Rong Wen · 2022
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Neural re-ranking in multi-stage recommender systems: A review
Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, and Ruiming Tang · 2022
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Intelligent request strategy design in recommender system
Xufeng Qian, Yue Xu, Fuyu Lv, Shengyu Zhang, Ziwen Jiang, Qingwen Liu, Xiaoyi Zeng, Tat-Seng Chua, and Fei Wu · 2022
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