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Found 2 papers, 1 papers with code
Multi-Objective Recommendation via Multivariate Policy Learning
We frame this as a decision-making task, where the scalarisation weights are actions taken to maximise an overall North Star reward (e. g. long-term user retention or growth).
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-$n$ Recommendation
Through a correlation analysis between off- and on-line experiments conducted on a large-scale recommendation platform, we show that our unbiased DCG estimates strongly correlate with online reward, even when some of the metric's inherent assumptions are violated.
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