UTURN aims to maximize the matching rate on its freight transport platform by efficiently connecting shippers with suitable carriers. To support this matching process, UTURN required a solution that was additive rather than restrictive on the platform. To achieve this, our research leverages recommendation systems and predictive models to help shipments find appropriate carriers in time while avoiding sending unnecessary email recommendations. We addressed three main questions: how to maximize matching probability through recommendations, how to balance the recommendation frequency to prevent spam, and how to ensure the solution adapts to market changes. We developed a recommendation system that ranks carriers based on their historical platform data using personalized k-nearest neighbour models and a custom similarity function. Tested in a controlled experiment, this system resulted in a 2.8% increase in the average matching rate, with improvements up to 3.4% in established regions and a peak of 6.2% in the Netherlands. However, the one-size-fits-all approach proved insufficient due to varying carrier capacities. To address the issue of deciding if and when to recommend, we introduced the Shipment Pulse Monitor (SPM), a decision tree-based predictive model that triggers recommendations when a shipment will likely be cancelled. To represent the trade-off between sending out recommendations to maximize matching probability and avoiding spam, we defined custom metrics for our problem. Our findings suggest that future work should focus on tailoring recommendations to specific carrier groups and combining hard cut-off points in data to allow a narrower focus for predictive models. Our work provides a comprehensive framework for improving matching rates on the UTURN platform with adaptive, targeted solutions.