Learning Recommendations from User Actions in the Item-poor Insurance Domain

Author:

Borg Bruun Simone1,Maistro Maria1,Lioma Christina1

Affiliation:

1. University of Copenhagen, Denmark

Publisher

ACM

Reference34 articles.

1. C.  C. Aggarwal . 2016. Context-Sensitive Recommender Systems . Springer International Publishing , 255–281. https://doi.org/10.1007/978-3-319-29659-3_8 C. C. Aggarwal. 2016. Context-Sensitive Recommender Systems. Springer International Publishing, 255–281. https://doi.org/10.1007/978-3-319-29659-3_8

2. Y. Bi , L. Song , M. Yao , Z. Wu , J. Wang , and J. Xiao . 2020. DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain . In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, (SIGIR 2020 ), J. Huang, Y. Chang, X. Cheng, J. Kamps, V. Murdock, J.-R. Wen, and Y. Liu (Eds.). ACM, 1661–1664. https://doi.org/10.1145/3397271.3401193 Y. Bi, L. Song, M. Yao, Z. Wu, J. Wang, and J. Xiao. 2020. DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, (SIGIR 2020), J. Huang, Y. Chang, X. Cheng, J. Kamps, V. Murdock, J.-R. Wen, and Y. Liu (Eds.). ACM, 1661–1664. https://doi.org/10.1145/3397271.3401193

3. K. Cho , B. van Merrienboer , D. Bahdanau , and Y. Bengio . 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches . In Proceedings of the 8th Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST@EMNLP 2014 ), D. Wu, M. Carpuat, X. Carreras, and E. M. Vecchi (Eds.). Association for Computational Linguistics, 103–111. https://doi.org/10.3115/v1/W14-4012 K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. In Proceedings of the 8th Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST@EMNLP 2014), D. Wu, M. Carpuat, X. Carreras, and E. M. Vecchi (Eds.). Association for Computational Linguistics, 103–111. https://doi.org/10.3115/v1/W14-4012

4. P. Cremonesi , Y. Koren , and R. Turrin . 2010. Performance of Recommender Algorithms on Top-n Recommendation Tasks . In Proceedings of the 2010 ACM Conference on Recommender Systems, (RecSys 2010 ), X. Amatriain, M. Torrens, P. Resnick, and M. Zanker (Eds.). ACM, 39–46. https://doi.org/10.1145/ 1864 708.1864721 P. Cremonesi, Y. Koren, and R. Turrin. 2010. Performance of Recommender Algorithms on Top-n Recommendation Tasks. In Proceedings of the 2010 ACM Conference on Recommender Systems, (RecSys 2010), X. Amatriain, M. Torrens, P. Resnick, and M. Zanker (Eds.). ACM, 39–46. https://doi.org/10.1145/1864708.1864721

5. J. Davidson , B. Liebald , J. Liu , P. Nandy , T. Van Vleet , U. Gargi , S. Gupta , Y. He , M. Lambert , B. Livingston , and D. Sampath . 2010. The YouTube Video Recommendation System . In Proceedings of the 4th ACM Conference on Recommender Systems, (RecSys 2010 ), X. Amatriain, M. Torrens, P. Resnick, and M. Zanker (Eds.). ACM, 293–296. https://doi.org/10.1145/ 1864 708.1864770 J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, and D. Sampath. 2010. The YouTube Video Recommendation System. In Proceedings of the 4th ACM Conference on Recommender Systems, (RecSys 2010), X. Amatriain, M. Torrens, P. Resnick, and M. Zanker (Eds.). ACM, 293–296. https://doi.org/10.1145/1864708.1864770

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