Different Formats of Value Functions in Ranking and Recommendation Systems: Advantages, Limitations, and Optimization Strategies
Keywords:
Value Functions, Recommendation Systems, Temporal Optimization, Reinforcement Learning, Hybrid ModelingAbstract
Value functions serve as the quantitative foundation of ranking and recommendation systems,translating user preferences and business objectives into actionable scoring mechanisms thatdetermine which content surfaces to billions of users daily. This article presents a comprehensive analysis of value function design across two critical dimensions
References
Ian MacKenzie, et al., "How retailers can keep up with consumers", mckinsey, October 1, 2013. https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers
Downloads
Published
2025-12-08
How to Cite
Abhishek Kumar. (2025). Different Formats of Value Functions in Ranking and Recommendation Systems: Advantages, Limitations, and Optimization Strategies . Journal of Computational Analysis and Applications (JoCAAA), 34(12), 253–268. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4342
Issue
Section
Articles
