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Research Article

Investigation on Mitigating Cold Start Delinquent in a Personalized Recommender System

Jyothis Unikkat1 Thomas Tilson2
1,2Dept. of CSE, Mahaguru Institute of Technology, Kerala, India

Published Online: November-December 2021

Pages: 04-06

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References

1. B. Amini, R. Ibrahim, and M. S. Othman, “Discovering the impact of knowledge inrecommender systems: Acomparative
study,”arXivPrepr.arXiv1109.0166,2011.
2. M.Zanker,“Acollaborativeconstraint-basedmeta-
levelrecommender,”inProceedingsofthe2008ACMconferenceonRecommendersystems,2008,pp.139–146.
3. N. Mirzadeh, F. Ricci, and M. Bansal, “Feature selection methods for conversational recommender systems,” in e-Technology, e-
Commerceande-Service,2005.EEE’05.Proceedings.The2005IEEEInternationalConferenceon,2005,pp.772–777.
4. B.Smyth,L.McGinty,J.Reilly,andK.McCarthy,“Compoundcritiques for conversational recommender systems,” in Proceedings
ofthe2004IEEE/WIC/ACMInternationalConferenceonWebIntelligence,2004,pp. 145–151.
5. A.Felfernig,G.Friedrich,D.Jannach,andM.Zanker,“Constraint-based recommender systems,” in Recommender
SystemsHandbook,Springer,2015,pp.161–190.

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