Recommendation systems in multi-stakeholder environments often require optimizing for multiple objectives simultaneously to meet supplier and consumer demands. Serving recommendations in these settings relies on efficiently combining the objectives to address each stakeholder’s expectations, often through a scalarization function with pre-determined and fixed weights. In practice, selecting these weights becomes a consequent problem. Recent work has developed algorithms that adapt these weights based on application-specific needs by using RL to train a model. While this solves for automatic weight computation, such approaches are not efficient for frequent weight adaptation. They also do not allow for human intervention oftentimes determined by business needs. To bridge this gap, we propose a novel multi-objective recommendation framework that is efficient for a small number of objectives. It also enables business decision makers to easily tune the optimization by assigning different importance to multiple objectives. We demonstrate the efficacy and efficiency of our framework through improvements in online business metrics.