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Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regression, factorization machines, neural networks and multi-armed bandits.
R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction . MIT press Cambridge, 1998, vol. 1, no. 1
1998
Earlier work this paper cites.
R. J. Mooney and L. Roy, “Content-based book recommending using learning for text categorization,” in ACM DL , 2000, pp. 195–204
2000
Earlier work this paper cites.
G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing , vol. 7, no. 1, pp. 76–80, 2003
2003
Earlier work this paper cites.
M. Deshpande and G. Karypis, “Item-based top- N recommendation algorithms,” ACM Trans. Inf. Syst. , vol. 22, no. 1, pp. 143–177, 2004
2004
Earlier work this paper cites.
G. Shani, D. Heckerman, and R. I. Brafman, “An mdp-based recommender system,” Journal of Machine Learning Research , vol. 6, pp. 1265–1295, 2005
2005
Earlier work this paper cites.
J. Wang, A. P. De Vries, and M. J. Reinders, “Unifying user-based and item-based collaborative filtering approaches by similarity fusion,” in SIGIR . ACM, 2006, pp. 501–508
2006
Earlier work this paper cites.
N. Taghipour and A. A. Kardan, “A hybrid web recommender system based on q-learning,” in Proceedings of the 2008 ACM Symposium on Applied Computing (SAC), Fortaleza, Ceara, Brazil, March 16-20, 2008 , 2008, pp. 1164–1168
2008
Earlier work this paper cites.
A. Mnih and R. R. Salakhutdinov, “Probabilistic matrix factorization,” in NIPS , 2008, pp. 1257–1264
2008
Earlier work this paper cites.
Y. Koren, “Factorization meets the neighborhood: a multifaceted collaborative filtering model,” in KDD . ACM, 2008, pp. 426–434
2008
Earlier work this paper cites.
Y. Koren, R. M. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” IEEE Computer , vol. 42, no. 8, pp. 30–37, 2009
2009
Earlier work this paper cites.
S. Rendle, “Factorization machines,” in ICDM, Sydney, Australia, 14-17 December 2010 , 2010, pp. 995–1000
2010
Earlier work this paper cites.
L. Li, W. Chu, J. Langford, and R. E. Schapire, “A contextual-bandit approach to personalized news article recommendation,” in WWW 2010, Raleigh, North Carolina, USA, April 26-30, 2010 , 2010, pp. 661–670
2010
Earlier work this paper cites.
L. Li, W. Chu, J. Langford, and R. E. Schapire, “A contextual-bandit approach to personalized news article recommendation,” in WWW . ACM, 2010, pp. 661–670
2010
Earlier work this paper cites.
O. Chapelle and L. Li, “An empirical evaluation of thompson sampling,” in NIPS, Granada, Spain. , 2011, pp. 2249–2257
2011
Earlier work this paper cites.
H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin, S. Chikkerur, D. Liu, M. Wattenberg, A. M. Hrafnkelsson, T. Boulos, and J. Kubica, “Ad click prediction: a view from the trenches,” in KDD 2013, Chicago, IL, USA, August 11-14, 2013 , 2013, pp. 1222–1230
2013
Cited alongside, same era.
X. Zhao, W. Zhang, and J. Wang, “Interactive collaborative filtering,” in CIKM’13, San Francisco, CA, USA, October 27 - November 1, 2013 , 2013, pp. 1411–1420
2013
Cited alongside, same era.
D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. A. Riedmiller, “Deterministic policy gradient algorithms,” in ICML 2014, Beijing, China, 21-26 June 2014 , 2014, pp. 387–395
2014
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. A. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015
H. Wang, Q. Wu, and H. Wang, “Learning hidden features for contextual bandits,” in CIKM . ACM, 2016, pp. 1633–1642
2016
Later among the works it cites.
H. Guo, R. Tang, Y. Ye, Z. Li, and X. He, “Deepfm: A factorization-machine based neural network for CTR prediction,” in IJCAI 2017, Melbourne, Australia, August 19-25, 2017 , 2017, pp. 1725–1731
2017
Later among the works it cites.
H. Wang, Q. Wu, and H. Wang, “Factorization bandits for interactive recommendation,” in AAAI, February 4-9, 2017, San Francisco, California, USA. , 2017, pp. 2695–2702
2017
Later among the works it cites.
H. Cai, K. Ren, W. Zhang, K. Malialis, J. Wang, Y. Yu, and D. Guo, “Real-time bidding by reinforcement learning in display advertising,” in WSDM 2017, Cambridge, United Kingdom, February 6-10, 2017 , 2017, pp. 661–670
2017
Later among the works it cites.
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2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Y. Juan, Y. Zhuang, W. Chin, and C. Lin, “Field-aware factorization machines for CTR prediction,” in RecSys, Boston, MA, USA, September 15-19, 2016 , 2016, pp. 43–50
2016
Cited alongside, same era.
W. Zhang, T. Du, and J. Wang, “Deep learning over multi-field categorical data - - A case study on user response prediction,” in ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings , 2016, pp. 45–57
2016
Cited alongside, same era.
Y. Qu, H. Cai, K. Ren, W. Zhang, Y. Yu, Y. Wen, and J. Wang, “Product-based neural networks for user response prediction,” in ICDM 2016, December 12-15, 2016, Barcelona, Spain , 2016, pp. 1149–1154
2016
Cited alongside, same era.
2016
Cited alongside, same era.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. P. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of go with deep neural networks and tree search,” Nature , vol. 529, no. 7587, pp. 484–489, 2016
2016
Cited alongside, same era.
L. Xia, J. Xu, Y. Lan, J. Guo, W. Zeng, and X. Cheng, “Adapting markov decision process for search result diversification,” in SIGIR , Shinjuku, Tokyo, Japan, August 7-11, 2017 , 2017, pp. 535–544
2017
Later among the works it cites.
Z. Wei, J. Xu, Y. Lan, J. Guo, and X. Cheng, “Reinforcement learning to rank with markov decision process,” in SIGIR , Shinjuku, Tokyo, Japan, August 7-11, 2017 , 2017, pp. 945–948
2017
Later among the works it cites.
G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan, X. Xie, and Z. Li, “DRN: A deep reinforcement learning framework for news recommendation,” in WWW 2018, Lyon, France, April 23-27, 2018 , 2018, pp. 167–176
2018
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2018
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H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang, “Efficient architecture search by network transformation,” in AAAI , New Orleans, Louisiana, USA, February 2-7, 2018 , 2018
2018
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2018
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2018
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2018
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C. Zeng, Q. Wang, S. Mokhtari, and T. Li, “Online context-aware recommendation with time varying multi-armed bandit,” in SIGKDD , San Francisco, CA, USA, August 13-17, 2016 , 2016, pp. 2025–2034
2034
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