LHRM: A LBS Based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform-Reference-Cited by-同舟云学术

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LHRM: A LBS Based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform

Published:2020 Issue: Volume: Page:479-490
ISSN:0302-9743
Container-title:Neural Information Processing
language:
Short-container-title:

Author:

Wang Ziyi,Xiao Wendong,Li Yu,Chen Zulong,Jiang Zhi

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-63836-8_40

Reference21 articles.

1. Bobadilla, J., Ortega, F., Hernando, A., Bernal, J.: A collaborative filtering approach to mitigate the new user cold start problem. Knowl. Based Syst. 26, 225–238 (2012). https://doi.org/10.1016/j.knosys.2011.07.021

2. Cao, L.: Coupling learning of complex interactions. Inf. Process. Manag. 51(2), 167–186 (2015). https://doi.org/10.1016/j.ipm.2014.08.007

3. Cao, L.: Non-IID recommender systems: a review and framework of recommendation paradigm shifting. Engineering 2(2), 212–224 (2016)

4. Cheng, H., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10 (2016). https://doi.org/10.1145/2988450.2988454

5. Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015, Florence, Italy, 18–22 May 2015, pp. 278–288 (2015). https://doi.org/10.1145/2736277.2741667

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