Managing taxi fleets in large cities is challenging, especially when drivers operate independently. This study improves taxi repositioning by predicting demand and providing smart recommendations. Using real-world data from a Scandinavian taxi service, we employ neural networks with LSTM layers to forecast demand and test different strategies, like a simple greedy algorithm and a more structured matching-based approach, to guide taxis to high-demand areas. Since drivers ultimately decide whether to follow these suggestions, we also model their behavior using a probabilistic acceptance strategy. Through a simulation of a real day, we analyze how different approaches impact passenger wait times and overall efficiency. The results show that proactive re-positioning significantly reduces wait times but can increase total driving distance. The greedy algorithm tends to perform better in quickly getting taxis to passengers, while the matching model is more efficient in minimizing unnecessary travel. However, increased mobility comes at a cost, as rerouting leads to longer driving distances. Additionally, driver behavior plays a crucial role, with lower acceptance rates reducing the effectiveness of predictive recommendations. Overestimating demand in such cases helps mitigate inefficiencies.