Applying multi-factor Beta distribution-based trust for improving accuracy of recommender systems
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17265-x.pdf
Reference53 articles.
1. Yao L et al (2019) Synergies between association rules and collaborative filtering in recommender system: An application to auto industry. Data science and digital business. Springer, pp 65–80
2. Rubens N et al (2015) Active learning in recommender systems. Recommender systems handbook. Springer, pp 809–846
3. Li Y-M, Wu C-T, Lai C-Y (2013) A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship. Decis Support Syst 55(3):740–752
4. Savage NS et al (2012) I’m feeling loco: A location based context aware recommendation system. Advances in Location-Based Services. Springer, pp 37–54
5. Bączkiewicz A et al (2021) Methodical aspects of MCDM based E-commerce recommender system. J Theor Appl Electron Commer Res 16(6):2192–2229
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