A contextual-bandit approach for multifaceted reciprocal recommendations in online dating

Author:

Kumari TulikaORCID,Sharma Ravish,Bedi Punam

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Reference48 articles.

1. Abbasi-Yadkori, Y., Pál, D., & Szepesvári, C. (2011). Improved algorithms for linear stochastic bandits. Advances in Neural Information Processing Systems, 24, 2312–2320.

2. Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., & Rej, T. (2011). Ccr—a content-collaborative reciprocal recommender for online dating. In Twenty-second international joint conference on artificial intelligence.

3. Alanazi, A., & Bain, M. (2016). A scalable people-to-people hybrid reciprocal recommender using hidden markov models. 2nd Int. Work. Mach. Learn. Methods Recomm Syst.

4. Altulyan, M.S., Huang, C., Yao, L., Wang, X., & Kanhere, S.S. (2021). Contextual bandit learning for activity-aware things-of-interest recommendation in an assisted living environment. In ADC (pp. 37–49).

5. Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2), 235–256.

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