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.

Audio fingerprinting has shown to be an effective approach to music identification, having properties robust to noise and signal degradations. A field in which audio fingerprinting has not been evaluated yet is music identification in movies. In movies, music is often accompanied with background noise, sound effects and dialogue, and further processed using mixing and mastering techniques. This paper evaluates the suitability of the audio fingerprinting framework `SoundFingerprinting' for the identification of music in movies. The framework is evaluated according to a benchmark established for this field. The framework was tested on actual movie data, noise-layered soundtracks, pitch-shifted soundtracks and tempo-changed soundtracks. The framework was unable to identify the music in actual movie data, thus directing the research to identify problematic areas specific to SoundFingerprinting. In identifying noise-layered soundtracks, the framework showed varying performance dependent on the dominant frequencies present in the noise sample. Furthermore, the framework showed to be robust to tempo-changes, whereas the framework was unable to identify pitch-shifted soundtracks. Based on this performance evaluation, SoundFingerprinting is ill-equipped for the task of music identification in movies.