Factual and Personalized Recommendations using Language Models and Reinforcement Learning
This work addresses the need for more engaging and accurate recommendations in conversational systems, though it appears incremental as it builds on existing language model and reinforcement learning techniques.
The paper tackled the problem of generating personalized and factual recommendations in conversational recommender systems by developing P4LM, a language model that uses embeddings and reinforcement learning, resulting in improved movie narratives on the MovieLens 25M dataset.
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.