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

Factual and Personalized Recommendations using Language Models and Reinforcement Learning