  Recommender systems (RSs) play a central role in connecting users to content,
products, and services, matching candidate items to users based on their
preferences. While traditional RSs rely on implicit user feedback signals,
conversational RSs interact with users in natural language. In this work, we
develop a comPelling, Precise, Personalized, Preference-relevant language model
(P4LM) that recommends items to users while putting emphasis on explaining item
characteristics and their relevance. P4LM uses the embedding space
representation of a user's preferences to generate compelling responses that
are factually-grounded and relevant w.r.t. the user's preferences. Moreover, we
develop a joint reward function that measures precision, appeal, and
personalization, which we use as AI-based feedback in a reinforcement
learning-based language model framework. Using the MovieLens 25M dataset, we
demonstrate that P4LM delivers compelling, personalized movie narratives to
users.
