Fetching the paper…
Reading the bibliography…
Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules.
P. Viola and W. M. Wells III, “Alignment by maximization of mutual information,” International journal of computer vision , vol. 24, no. 2, pp. 137–154, 1997
1997
Earlier work this paper cites.
L. H. Ungar and D. P. Foster, “Clustering methods for collaborative filtering,” in AAAI workshop on recommendation systems , vol. 1. Menlo Park, CA, 1998, pp. 114–129
1998
Earlier work this paper cites.
R. Van Meteren and M. Van Someren, “Using content-based filtering for recommendation,” in Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop , vol. 30, 2000, pp. 47–56
2000
Earlier work this paper cites.
T. Tran and R. Cohen, “Hybrid recommender systems for electronic commerce,” in Proc. Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, Technical Report WS-00-04, AAAI Press , vol. 40, 2000
2000
Earlier work this paper cites.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web , 2001, pp. 285–295
2001
Earlier work this paper cites.
Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, “A neural probabilistic language model,” Journal of machine learning research , vol. 3, no. Feb, pp. 1137–1155, 2003
2003
Earlier work this paper cites.
X. Su and T. M. Khoshgoftaar, “Collaborative filtering for multi-class data using belief nets algorithms,” in 2006 18th IEEE international conference on Tools with Artificial Intelligence (ICTAI’06) . IEEE, 2006, pp. 497–504
2006
Earlier work this paper cites.
J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen, “Collaborative filtering recommender systems,” in The adaptive web . Springer, 2007, pp. 291–324
2007
Earlier work this paper cites.
Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” in 2008 Eighth IEEE international conference on data mining . Ieee, 2008, pp. 263–272
2008
Earlier work this paper cites.
Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer , vol. 42, no. 8, pp. 30–37, 2009
2009
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 Proceedings of the 19th WWW , 2010, pp. 661–670
2010
Earlier work this paper cites.
P. Lops, M. d. Gemmis, and G. Semeraro, “Content-based recommender systems: State of the art and trends,” Recommender systems handbook , pp. 73–105, 2011
2011
Earlier work this paper cites.
2013
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in neural information processing systems , 2013, pp. 3111–3119
2013
Earlier work this paper cites.
J. Daiber, M. Jakob, C. Hokamp, and P. N. Mendes, “Improving efficiency and accuracy in multilingual entity extraction,” in Proceedings of the 9th international conference on semantic systems , 2013, pp. 121–124
2013
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
X. Wang and A. Gupta, “Unsupervised learning of visual representations using videos,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2794–2802
2015
Earlier work this paper cites.
A. Sordoni, Y. Bengio, H. Vahabi, C. Lioma, J. Grue Simonsen, and J.-Y. Nie, “A hierarchical recurrent encoder-decoder for generative context-aware query suggestion,” in proceedings of the 24th ACM international on conference on information and knowledge management , 2015, pp. 553–562
2015
Earlier work this paper cites.
S. Li, A. Karatzoglou, and C. Gentile, “Collaborative filtering bandits,” in Proceedings of the 39th International ACM SIGIR , 2016, pp. 539–548
2016
Earlier work this paper cites.
K. Christakopoulou, F. Radlinski, and K. Hofmann, “Towards conversational recommender systems,” in Proceedings of the 22nd ACM SIGKDD , 2016, pp. 815–824
2016
Earlier work this paper cites.
J. Gu, Z. Lu, H. Li, and V. O. Li, “Incorporating copying mechanism in sequence-to-sequence learning,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Berlin, Germany: Association for Computational Linguistics, Aug. 2016, pp. 1631–1640. [Online]. Available: https://aclanthology.org/P16-1154
2016
Earlier work this paper cites.
C. Gulcehre, S. Ahn, R. Nallapati, B. Zhou, and Y. Bengio, “Pointing the unknown words,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Berlin, Germany: Association for Computational Linguistics, Aug. 2016, pp. 140–149. [Online]. Available: https://aclanthology.org/P16-1014
2016
Cited alongside, same era.
2016
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, Eds., 2017, pp. 5998–6008
2017
Cited alongside, same era.
K. Zhou, W. X. Zhao, S. Bian, Y. Zhou, J.-R. Wen, and J. Yu, “Improving conversational recommender systems via knowledge graph based semantic fusion,” in Proceedings of the 26th ACM SIGKDD , 2020, pp. 1006–1014
2020
Later among the works it cites.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistics, Jul. 2020, pp. 7871–7880. [Online]. Available: https://aclanthology.org/2020.acl-main.703
2020
Later among the works it cites.
W. Lei, X. He, Y. Miao, Q. Wu, R. Hong, M.-Y. Kan, and T.-S. Chua, “Estimation-action-reflection: Towards deep interaction between conversational and recommender systems,” in Proceedings of the 13th International Conference on Web Search and Data Mining , 2020, pp. 304–312
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
K. Christakopoulou, A. Beutel, R. Li, S. Jain, and E. H. Chi, “Q&r: A two-stage approach toward interactive recommendation,” in Proceedings of the 24th ACM SIGKDD , 2018, pp. 139–148
2018
Cited alongside, same era.
Y. Zhang, X. Chen, Q. Ai, L. Yang, and W. B. Croft, “Towards conversational search and recommendation: System ask, user respond,” in Proceedings of the 27th acm international conference on information and knowledge management , 2018, pp. 177–186
2018
Cited alongside, same era.
Y. Sun and Y. Zhang, “Conversational recommender system,” in The 41st international acm sigir conference on research & development in information retrieval , 2018, pp. 235–244
2018
Cited alongside, same era.
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in European semantic web conference . Springer, 2018, pp. 593–607
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Q. Chen, J. Lin, Y. Zhang, M. Ding, Y. Cen, H. Yang, and J. Tang, “Towards knowledge-based recommender dialog system,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . Hong Kong, China: Association for Computational Linguistics, Nov. 2019, pp. 1803–1813. [Online]. Available: https://aclanthology.org/D19-1189
2019
Cited alongside, same era.
X. Ren, H. Yin, T. Chen, H. Wang, N. Q. V. Hung, Z. Huang, and X. Zhang, “Crsal: Conversational recommender systems with adversarial learning,” ACM Transactions on Information Systems (TOIS) , vol. 38, no. 4, pp. 1–40, 2020
2020
Later among the works it cites.
H. Xu, S. Moon, H. Liu, B. Liu, P. Shah, B. Liu, and P. Yu, “User memory reasoning for conversational recommendation,” in Proceedings of the 28th International Conference on Computational Linguistics . Barcelona, Spain (Online): International Committee on Computational Linguistics, Dec. 2020, pp. 5288–5308. [Online]. Available: https://aclanthology.org/2020.coling-main.463
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
K. Xu, J. Yang, J. Xu, S. Gao, J. Guo, and J.-R. Wen, “Adapting user preference to online feedback in multi-round conversational recommendation,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining , 2021, pp. 364–372
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Closest in time.
W. Lei, G. Zhang, X. He, Y. Miao, X. Wang, L. Chen, and T. Chua, “Interactive path reasoning on graph for conversational recommendation,” in KDD ’20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020 , R. Gupta, Y. Liu, J. Tang, and B. A. Prakash, Eds. ACM, 2020, pp. 2073–2083
2083
Closest in time.