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Recommender systems have made significant strides in various industries, primarily driven by extensive efforts to enhance recommendation accuracy.
The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Jaime G. Carbonell and Jade Goldstein. 1998 · 1998
Earlier work this paper cites.
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
Greg Linden, Brent Smith, and Jeremy York. 2003 · 2003
Earlier work this paper cites.
A content recommendation system based on category correlations. In 2010 Fifth International Multi-conference on Computing in the Global Information Technology . IEEE, 66–70
Sang-Min Choi and Yo-Sub Han. 2010 · 2010
Earlier work this paper cites.
Helping users perceive recommendation diversity.. In DiveRS@ RecSys . 43–50
Rong Hu and Pearl Pu. 2011 · 2011
Earlier work this paper cites.
The filter bubble: What the Internet is hiding from you
Eli Pariser. 2011 · 2011
Earlier work this paper cites.
Diversity maximization under matroid constraints. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining . 32–40
Zeinab Abbassi, Vahab S Mirrokni, and Mayur Thakur. 2013 · 2013
Earlier work this paper cites.
Deep Metric Learning Using Triplet Network. In Similarity-Based Pattern Recognition . Springer International Publishing, Cham, 84–92
Elad Hoffer and Nir Ailon. 2015 · 2015
Earlier work this paper cites.
Wide & deep learning for recommender systems. In the 1st workshop on deep learning for recommender systems . 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 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.
Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention
Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Cited alongside, same era.
Diversity in recommender systems – A survey
Matevž Kunaver and Tomaž Požrl. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Deep learning using rectified linear units (relu)
Abien Fred Agarap. 2018 · 2018
Cited alongside, same era.
On the Convergence of Adam and Beyond. In 6th International Conference on Learning Representations, ICLR 2018 . OpenReview.net
Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar. 2018 · 2018
Controllable multi-interest framework for recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2942–2951
Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. 2020 · 2020
Later among the works it cites.
Understanding echo chambers in e-commerce recommender systems. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval
Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, and Yongfeng Zhang. 2020 · 2020
Later among the works it cites.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020 · 2020
Later among the works it cites.
Deep multi-interest network for click-through rate prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Zhibo Xiao, Luwei Yang, Wen Jiang, Yi Wei, Yi Hu, and Hao Wang. 2020 · 2020
Later among the works it cites.
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Cited alongside, same era.
Practical Diversified Recommendations on YouTube with Determinantal Point Processes. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management
Mark Wilhelm, Ajith Ramanathan, Alexander Bonomo, Sagar Jain, Ed H. Chi, and Jennifer Gillenwater. 2018 · 2018
Cited alongside, same era.
Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 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.
Multi-interest network with dynamic routing for recommendation at Tmall. In Proceedings of the 28th ACM international conference on information and knowledge management . 2615–2623
Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019 · 2019
Cited alongside, same era.
NPA: Neural News Recommendation with Personalized Attention. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. 2019 · 2019
Cited alongside, same era.
Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 5941–5948
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019 · 2019
Cited alongside, same era.
Sparse-Interest Network for Sequential Recommendation. In WSDM ’21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021 . ACM, 598–606
Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, and Xia Hu. 2021 · 2021
Later among the works it cites.
Dynamic graph construction for improving diversity of recommendation. In Proceedings of the 15th ACM Conference on Recommender Systems
Rui Ye, Yuqing Hou, Te Lei, Yunxing Zhang, Qing Zhang, Jiale Guo, Huaiwen Wu, and Hengliang Luo. 2021 · 2021
Later among the works it cites.
DGCN: Diversified recommendation with graph convolutional networks. In Proceedings of the Web Conference
Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, and Yong Li. 2021 · 2021
Later among the works it cites.
A Survey of Diversification Techniques in Search and Recommendation
Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Fernando Diaz, and Xue Liu. 2022 · 2022
Later among the works it cites.
How Recommendation Affects Customer Search: A Field Experiment
Zhe Yuan, AJ Chen, Y Wang, and T Sun. 2022 · 2022
Later among the works it cites.
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong liu, Taiqing Wang, and Philip S. Yu. 2023 · 2023
Later among the works it cites.