Cold Start in Recommender Systems—A Survey from Domain Perspective-Reference-Cited by-同舟云学术

Cold Start in Recommender Systems—A Survey from Domain Perspective

Published:2021 Issue: Volume: Page:223-232
ISSN:2367-4512
Container-title:Intelligent Data Communication Technologies and Internet of Things
language:
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Author:

Sethi Rachna,Mehrotra Monica

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-9509-7_19

Reference37 articles.

1. Jones AM et al (2017) An ontological sub-matrix factorization based approach for cold-start issue in recommender systems. In: International conference on current trends in computer, electrical, electronics and communication (ICCTCEEC), 8–9, September 2017, Mysuru, India

2. Liphoto M et al (2016) A survey on recommender systems. In: Third international conference on advances in computing, communication and engineering: ICACCE 2016: proceedings, 28–29 November 2016, Durban, South Africa

3. Selva Rani B, Ananda Kumar S (2019) Mitigating cold start problem in a personalised recommender system. Int J Innov Technol Explor Eng (IJITEE) 8(7):212–216. ISSN 2278-3075, ISSN: 2278-3075

4. Gope J, Jain SK (2017) A survey on solving cold start problem in recommender systems. In: IEEE international conference on computing, communication and automation (ICCCA 2017), 5–6 May 2017. https://doi.org/10.1109/ccaa.2017.8229786

5. Sun M et al (2018) A multi-modality deep network for cold-start recommendation. Big Data Cogn Comput 2(1):7. https://doi.org/10.3390/bdcc2010007

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