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We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together.
Self-organization in a perceptual network
Ralph Linsker. 1988 · 1988
Earlier work this paper cites.
TV program recommendation for multiple viewers based on user profile merging
Zhiwen Yu, Xingshe Zhou, Yanbin Hao, and Jianhua Gu. 2006 · 2006
Earlier work this paper cites.
Group recommendation: Semantics and efficiency
Sihem Amer-Yahia, Senjuti Basu Roy, Ashish Chawlat, Gautam Das, and Cong Yu. 2009 · 2009
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback. In UAI . AUAI Press, 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Group recommendations with rank aggregation and collaborative filtering. In RecSys . 119–126
Linas Baltrunas, Tadas Makcinskas, and Francesco Ricci. 2010 · 2010
Earlier work this paper cites.
Group-based recipe recommendations: analysis of data aggregation strategies. In RecSys . ACM, 111–118
Shlomo Berkovsky and Jill Freyne. 2010 · 2010
Earlier work this paper cites.
State-of-the-art in group recommendation and new approaches for automatic identification of groups
Ludovico Boratto and Salvatore Carta. 2010 · 2010
Earlier work this paper cites.
Performance of recommender algorithms on top-n recommendation tasks. In RecSys . 39–46
Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010 · 2010
Earlier work this paper cites.
Enhancing group recommendation by incorporating social relationship interactions. In GROUP . 97–106
Mike Gartrell, Xinyu Xing, Qin Lv, Aaron Beach, Richard Han, Shivakant Mishra, and Karim Seada. 2010 · 2010
Earlier work this paper cites.
Rank and relevance in novelty and diversity metrics for recommender systems. In RecSys . ACM, 109–116
Saúl Vargas and Pablo Castells. 2011 · 2011
Earlier work this paper cites.
Exploring personal impact for group recommendation. In CIKM . ACM, 674–683
Xingjie Liu, Yuan Tian, Mao Ye, and Wang-Chien Lee. 2012 · 2012
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Deep modeling of group preferences for group-based recommendation. In AAAI
Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, and Wei Cao. 2014 · 2014
Earlier work this paper cites.
Exploiting geographical neighborhood characteristics for location recommendation. In CIKM . ACM, 739–748
Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014 · 2014
Earlier work this paper cites.
COM: a generative model for group recommendation. In KDD . ACM, 163–172
Quan Yuan, Gao Cong, and Chin-Yew Lin. 2014 · 2014
Cited alongside, same era.
Recommending new items to ephemeral groups using contextual user influence. In RecSys . ACM, 285–292
Elisa Quintarelli, Emanuele Rabosio, and Letizia Tanca. 2016 · 2016
Cited alongside, same era.
Probabilistic group recommendation model for crowdfunding domains. In WSDM . ACM, 257–266
Vineeth Rakesh, Wang-Chien Lee, and Chandan K Reddy. 2016 · 2016
Cited alongside, same era.
Inductive representation learning on large graphs. In NIPS . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Neural collaborative filtering. In WWW . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch. In NIPS-W
Learning to Reweight Examples for Robust Deep Learning. In ICML . 4331–4340
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. 2018 · 2018
Later among the works it cites.
Identifying Dominators and Followers in Group Decision Making based on The Personality Traits. In IUI Workshops
Yong Zheng. 2018 · 2018
Later among the works it cites.
Social-Enhanced Attentive Group Recommendation
Da Cao, Xiangnan He, Lianhai Miao, Guangyi Xiao, Hao Chen, and Jiao Xu. 2019 · 2019
Later among the works it cites.
Learning deep representations by mutual information estimation and maximization. In ICLR
R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio. 2019 · 2019
Later among the works it cites.
A modular adversarial approach to social recommendation. In CIKM . 1753–1762
Adit Krishnan, Hari Cheruvu, Cheng Tao, and Hari Sundaram. 2019 · 2019
Later among the works it cites.
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Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Cited alongside, same era.
Deep sets. In NIPS . 3391–3401
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola. 2017 · 2017
Cited alongside, same era.
Mutual Information Neural Estimation. In ICML . 530–539
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Devon Hjelm, and Aaron Courville. 2018 · 2018
Cited alongside, same era.
Attentive group recommendation. In SIGIR . ACM, 645–654
Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, and Richang Hong. 2018 · 2018
Cited alongside, same era.
An observational user study for group recommender systems in the tourism domain
Amra Delic, Julia Neidhardt, Thuy Ngoc Nguyen, and Francesco Ricci. 2018b · 2018
Cited alongside, same era.
An adversarial approach to improve long-tail performance in neural collaborative filtering. In CIKM . 1491–1494
Adit Krishnan, Ashish Sharma, Aravind Sankar, and Hari Sundaram. 2018 · 2018
Cited alongside, same era.
Variational autoencoders for collaborative filtering. In WWW . 689–698
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018 · 2018
Cited alongside, same era.
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019 · 2019
Later among the works it cites.
Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation. In SIGIR . ACM, 255–264
Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, Gao Cong, and Xiaoli Li. 2019 · 2019
Later among the works it cites.
Group Recommendation via Self-Attention and Collaborative Metric Learning Model
Haiyan Wang, Yuliang Li, and Felix Frimpong. 2019 · 2019
Later among the works it cites.
How Powerful are Graph Neural Networks?. In ICLR
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Later among the works it cites.
QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization. In EMNLP . 3368–3373
Yi-Ting Yeh and Yun-Nung Chen. 2019 · 2019
Later among the works it cites.
Social influence-based group representation learning for group recommendation. In ICDE . IEEE, 566–577
Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Jiali Yang, and Xiaofang Zhou. 2019 · 2019
Later among the works it cites.
Deep Learning Based Recommender System: A Survey and New Perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019 · 2019
Later among the works it cites.
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
Adit Krishnan, Mahashweta Das, Mangesh Bendre, Hao Yang, and Hari Sundaram. 2020 · 2020
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