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Recommender systems trained on offline historical user behaviors are embracing conversational techniques to online query user preference.
Methods and metrics for cold-start recommendations
Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock · 2002
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Influence-based collaborative active learning
Neil Rubens and Masashi Sugiyama · 2007
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Tied boltzmann machines for cold start recommendations
Asela Gunawardana and Christopher Meek · 2008
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Addressing cold-start problem in recommendation systems
Xuan Nhat Lam, Thuc Vu, Trong Duc Le, and Anh Duc Duong · 2008
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Regression-based latent factor models
Deepak Agarwal and Bee-Chung Chen · 2009
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Pairwise preference regression for cold-start recommendation
Seung-Taek Park and Wei Chu · 2009
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On bootstrapping recommender systems
Nadav Golbandi, Yehuda Koren, and Ronny Lempel · 2010
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Factorization machines
Steffen Rendle · 2010
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https://grouplens.org/datasets/hetrec-2011/
LastFM Dataset · 2011
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Adaptive bootstrapping of recommender systems using decision trees
Nadav Golbandi, Yehuda Koren, and Ronny Lempel · 2011
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Long short-term memory
Alex Graves and Alex Graves · 2012
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Bpr: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme · 2012
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A comparative study of decision tree id3 and c4. 5
Badr Hssina, Abdelkarim Merbouha, Hanane Ezzikouri, and Mohammed Erritali · 2014
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Item cold-start recommendations: learning local collective embeddings
Martin Saveski and Amin Mantrach · 2014
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Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk · 2015
Cited alongside, same era.
Improving top-n recommendation for cold-start users via cross-domain information
Nima Mirbakhsh and Charles X Ling · 2015
Cited alongside, same era.
Active learning in recommender systems
Neil Rubens, Mehdi Elahi, Masashi Sugiyama, and Dain Kaplan · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Towards conversational recommender systems
Konstantina Christakopoulou, Filip Radlinski, and Katja Hofmann · 2016
Cited alongside, same era.
A survey of active learning in collaborative filtering recommender systems
Conversational recommender system
Yueming Sun and Yi Zhang · 2018
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Towards conversational search and recommendation: System ask, user respond
Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W Bruce Croft · 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
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Conversational product search based on negative feedback
Keping Bi, Qingyao Ai, Yongfeng Zhang, and W Bruce Croft · 2019
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A visual dialog augmented interactive recommender system
Tong Yu, Yilin Shen, and Hongxia Jin · 2019
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Deep interest evolution network for click-through rate prediction
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai · 2019
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Mehdi Elahi, Francesco Ricci, and Neil Rubens · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Addressing complex and subjective product-related queries with customer reviews
Julian McAuley and Alex Yang · 2016
Cited alongside, same era.
Product-based neural networks for user response prediction
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang · 2016
Cited alongside, same era.
Deepfm: a factorization-machine based neural network for ctr prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 2017
Cited alongside, same era.
Neural collaborative filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua · 2017
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Cited alongside, same era.
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Dtcdr: A framework for dual-target cross-domain recommendation
Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng · 2019
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Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang · 2020
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Estimation-action-reflection: Towards deep interaction between conversational and recommender systems
Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, and Tat-Seng Chua · 2020
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Conversational contextual bandit: Algorithm and application
Xiaoying Zhang, Hong Xie, Hang Li, and John CS Lui · 2020
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Catn: Cross-domain recommendation for cold-start users via aspect transfer network
Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, and Aixin Sun · 2020
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Hierarchy-aware label semantics matching network for hierarchical text classification
Haibin Chen, Qianli Ma, Zhenxi Lin, and Jiangyue Yan · 2021
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Advances and challenges in conversational recommender systems: A survey
Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua · 2021
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A survey on conversational recommender systems
Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen · 2021
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Task ambiguity in humans and language models
Alex Tamkin, Kunal Handa, Avash Shrestha, and Noah Goodman · 2022
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