Factual and Personalized Recommendations using Language Models and Reinforcement Learning

• URL: http://arxiv.org/abs/2310.06176v1
• Date: Mon, 9 Oct 2023 21:58:55 GMT
• Title: Factual and Personalized Recommendations using Language Models and Reinforcement Learning
• Authors: Jihwan Jeong, Yinlam Chow, Guy Tennenholtz, Chih-Wei Hsu, Azamat Tulepbergenov, Mohammad Ghavamzadeh, Craig Boutilier
• Abstract summary: We develop a comPelling, Precise, Personalized, Preference-relevant language model (P4LM) P4LM recommends items to users while putting emphasis on explaining item characteristics and their relevance. We develop a joint reward function that measures precision, appeal, and personalization.
• Score: 38.96462170594542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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