Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself

Traditional recommender systems are typically trained to predict what item users will interact with next, but not why. However, offering personalized evidence for why a user might like the predicted item is an important way to enhance the service and to raise the likelihood that the user will be gen